Daniel H. Wolf
University of Pennsylvania
PsychiatryPsychologyNeuroscienceClinical psychologySchizophrenia
What is this?
Publications 119
#1Laura Han (VU: VU University Amsterdam)H-Index: 3
#2Richard Dinga (Radboud University Nijmegen)H-Index: 2
Last. Klaus Berger (WWU: University of Münster)H-Index: 70
view all 146 authors...
Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18–75 yea...
#1Scott W. Woods (Yale University)H-Index: 77
#2Carrie E. Bearden (Semel Institute for Neuroscience and Human Behavior)H-Index: 59
Last. Albert R. Powers (Yale University)H-Index: 10
view all 36 authors...
Abstract Background Malhi et al. in this issue critique the clinical high risk (CHR) syndrome for psychosis. Method Response to points of critique. Results We agree that inconsistency in CHR nomenclature should be minimized. We respectfully disagree on other points. In our view: a) individuals with CHR and their families need help, using existing interventions, even though we do not yet fully understand disease mechanisms; b) substantial progress has been made in identification of biomarkers; c)...
#1Sunny X. TangH-Index: 7
#2Reno KrizH-Index: 1
Last. Mark LibermanH-Index: 32
view all 10 authors...
#1Danai Dima (City University London)H-Index: 20
#2Efstathios Papachristou (UCL: University College London)H-Index: 9
Last. Kathryn I. AlpertH-Index: 11
view all 197 authors...
Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalised on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine the age-related morphometric trajectories of the ventric...
#1Ganesh B. ChandH-Index: 5
#2Daniel H. WolfH-Index: 39
Last. Alessandro PigoniH-Index: 1
view all 29 authors...
#1Sophia Frangou (ISMMS: Icahn School of Medicine at Mount Sinai)H-Index: 62
#2Amirhossein Modabbernia (ISMMS: Icahn School of Medicine at Mount Sinai)H-Index: 23
Last. Dag Alnæs (University of Oslo)H-Index: 18
view all 196 authors...
Delineating age-related cortical trajectories in healthy individuals is critical given the association of cortical thickness with cognition and behaviour. Previous research has shown that deriving robust estimates of age-related brain morphometric changes requires large-scale studies. In response, we conducted a large-scale analysis of cortical thickness in 17,075 individuals aged 3-90 years by pooling data through the Lifespan Working group of the Enhancing Neuroimaging Genetics through Meta-An...
1 CitationsSource
#1Daniel H. Wolf (UPenn: University of Pennsylvania)H-Index: 39
#2Ganesh B. Chand (UPenn: University of Pennsylvania)H-Index: 5
Last. Alessandro Pigoni (LMU: Ludwig Maximilian University of Munich)H-Index: 1
view all 28 authors...