Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction

scientific article

Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction is …
instance of (P31):
scholarly articleQ13442814

External links are
P356DOI10.1002/GPS.4262
P932PMC publication ID4683603
P698PubMed publication ID25689482

P50authorCharles F. Reynolds IIIQ29669051
Howard J AizensteinQ42374777
P2093author name stringCarmen Andreescu
Julie C Price
Kathryn L Edelman
Meenal J Patel
P2860cites workFrontal white matter anisotropy and antidepressant remission in late-life depressionQ21144232
Anterior cingulate cortical volumes and treatment remission of geriatric depressionQ24642093
Early onset of selective serotonin reuptake inhibitor antidepressant action: systematic review and meta-analysisQ24647799
Are antidepressant drugs that combine serotonergic and noradrenergic mechanisms of action more effective than the selective serotonin reuptake inhibitors in treating major depressive disorder? A meta-analysis of studies of newer agentsQ28307397
FSLQ29616228
Magnetic resonance imaging predictors of treatment response in late-life depressionQ30724972
Brain network dysfunction in late-life depression: a literature reviewQ30724976
Relationship between baseline white-matter changes and development of late-life depressive symptoms: 3-year results from the LADIS studyQ39956247
White matter hyperintensities, cortisol levels, brain atrophy and continuing cognitive deficits in late-life depressionQ43179371
Conn: a functional connectivity toolbox for correlated and anticorrelated brain networksQ44033763
Prediction of illness severity in patients with major depression using structural MR brain scansQ44892036
Aging and late-life depressionQ45161253
Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.Q45960669
Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.Q45962941
Prevalence and gender differences in late-life depression: a population-based studyQ46418438
The concept of cognitive reserve: a catalyst for research.Q48276130
Abnormal regional spontaneous neural activity in first-episode, treatment-naive patients with late-life depression: a resting-state fMRI study.Q48440326
Relationship between progression of brain white matter changes and late-life depression: 3-year results from the LADIS study.Q48495540
Neuroanatomic localization of magnetic resonance imaging signal hyperintensities in geriatric depression.Q48512076
Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis.Q48614919
Regional cerebral blood flow in late-life depression: arterial spin labelling magnetic resonance study.Q48757116
Association between the prevalence of depression and age in a large representative German sample of people aged 53 to 80 years.Q48863203
Does education moderate neuropsychological impairment in late-life depression?Q48913698
Neuroanatomy of verbal working memory as a diagnostic biomarker for depression.Q48922836
White matter changes in late-life depression: a diffusion tensor imaging study.Q48937174
Depressive symptoms and cognitive decline in late life: a prospective epidemiological study.Q50481302
Depressive state- and disease-related alterations in neural responses to affective and executive challenges in geriatric depression.Q50793325
Pattern classification of sad facial processing: toward the development of neurobiological markers in depression.Q50884193
The pattern and course of cognitive impairment in late-life depression.Q51865017
Support-vector networksQ55922708
Comparative efficacy of selective serotonin reuptake inhibitors and tricyclics in the treatment of melancholiaQ72850780
Region of interest based analysis of functional imaging dataQ30821794
A systematic review of antidepressant placebo-controlled trials for geriatric depression: limitations of current data and directions for the futureQ30955590
Diffuse microstructural abnormalities of normal-appearing white matter in late life depression: a diffusion tensor imaging studyQ33431542
Prognostic and diagnostic potential of the structural neuroanatomy of depressionQ33487168
Education and risk for late life depression: a meta-analysis of published literatureQ33610489
Empirically derived decision trees for the treatment of late-life depressionQ33734203
Age- and gender-specific prevalence of depression in latest-life--systematic review and meta-analysis.Q33782865
Altered cerebellar-cerebral functional connectivity in geriatric depressionQ33921308
Structural integrity of the uncinate fasciculus and resting state functional connectivity of the ventral prefrontal cortex in late life depressionQ33974959
Integrating neurobiological markers of depression.Q34153576
MRI signal hyperintensities and treatment remission of geriatric depressionQ34160970
Biological basis of late life depressionQ34259252
Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scansQ34345460
One-year change in anterior cingulate cortex white matter microstructure: relationship with late-life depression outcomesQ34398160
A systematic review and meta-analysis of magnetic resonance imaging studies in late-life depressionQ34560092
Microstructural white matter abnormalities and remission of geriatric depressionQ34732676
Reduction of dorsolateral prefrontal cortex gray matter in late-life depressionQ35017230
Remission with venlafaxine extended release or selective serotonin reuptake inhibitors in depressed patients: a randomized, open-label studyQ35062660
Default-mode network connectivity and white matter burden in late-life depressionQ35296274
The default mode network in late-life anxious depressionQ35434961
A fully automated method for quantifying and localizing white matter hyperintensities on MR imagesQ35568739
Decoding subject-driven cognitive states with whole-brain connectivity patternsQ35610139
Diminished performance on neuropsychological testing in late life depression is correlated with microstructural white matter abnormalitiesQ35915903
Functional connectivity in the cognitive control network and the default mode network in late-life depressionQ35923089
Resting-state functional connectivity in late-life depression: higher global connectivity and more long distance connectionsQ36520054
fMRI correlates of white matter hyperintensities in late-life depressionQ36850701
Late life depression with cognitive impairment: evaluation and treatmentQ37199884
Structural brain changes as biomarkers and outcome predictors in patients with late-life depression: a cross-sectional and prospective study.Q37308873
Graph theory analysis of cortical-subcortical networks in late-life depressionQ37381117
Resting state functional connectivity and treatment response in late-life depressionQ37397964
Major depression: the importance of clinical characteristics and treatment response to prognosisQ37607162
How late-life depression affects cognition: neural mechanismsQ37738400
Late-life depression: evidence-based treatment and promising new directions for research and clinical practiceQ37871155
Review: antidepressants are effective for the treatment of major depressive disorder in individuals aged 55 years or olderQ38009693
P4510describes a project that usesscikit-imageQ22442795
P433issue10
P407language of work or nameEnglishQ1860
P921main subjectmachine learningQ2539
P304page(s)1056-1067
P577publication date2015-02-17
P1433published inInternational Journal of Geriatric PsychiatryQ6051406
P1476titleMachine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction
P478volume30

Reverse relations

cites work (P2860)
Q90401138A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers
Q92730336A Future Research Agenda for Digital Geriatric Mental Healthcare
Q64997897Acute trajectories of neural activation predict remission to pharmacotherapy in late-life depression.
Q51766523Advances and Barriers for Clinical Neuroimaging in Late-Life Mood and Anxiety Disorders.
Q36269149Altered resting-state functional connectivity in late-life depression: A cross-sectional study
Q96589095Automated classification of depression from structural brain measures across two independent community-based cohorts
Q34548237Computational neuroscience approach to biomarkers and treatments for mental disorders
Q57169000Development and evaluation of a multimodal marker of major depressive disorder
Q90728377Forty years of structural brain imaging in mental disorders: is it clinically useful or not?
Q91564609Increased ventromedial prefrontal cortex activity and connectivity predict poor sertraline treatment outcome in late-life depression
Q92260246Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia
Q57167744Machine learning in major depression: From classification to treatment outcome prediction
Q62492840Machine learning studies on major brain diseases: 5-year trends of 2014-2018
Q39475760Neural Predictors of Initiating Alcohol Use During Adolescence
Q92911062Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach
Q89551935Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches
Q30971457Realizing the potential of mobile mental health: new methods for new data in psychiatry
Q36308028Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study.
Q31063019Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Q26772163Studying depression using imaging and machine learning methods
Q45945305The revolution of personalized psychiatry: will technology make it happen sooner?
Q94566937Towards a brain-based predictome of mental illness
Q91866864Whole-Brain Exploratory Analysis of Functional Task Response Following Erythropoietin Treatment in Mood Disorders: A Supervised Machine Learning Approach

Search more.