Results 221 to 230 of about 550,647 (331)

Feature-Based Anticipation of Cues that Predict Reward in Monkey Caudate Nucleus [PDF]

open access: bronze, 2002
Johan Lauwereyns   +7 more
openalex   +1 more source

Pattern of Deep Grey Matter Undersizing Boosts MRI‐Based Diagnostic Classifiers in Fetal Alcohol Spectrum Disorders

open access: yesHuman Brain Mapping, Volume 46, Issue 8, June 1, 2025.
Using MRI‐based normative scaling analysis, we identified a robust pattern of deep grey matter undersizing in fetal alcohol syndrome (FAS). This novel neuroanatomical marker boosted the performances of brain size‐based classifiers and their generalizability to fetal alcohol spectrum disorders lacking FAS‐specific clinical features, supporting its use ...
Eliot Kerdreux   +10 more
wiley   +1 more source

Reduced GLP-1R availability in the caudate nucleus with Alzheimer's disease. [PDF]

open access: yesFront Aging Neurosci
Barrett E   +9 more
europepmc   +1 more source

Hierarchical Organization of Bilateral Prefrontal‐Basal Ganglia Circuits for Response Inhibition Control

open access: yesHuman Brain Mapping, Volume 46, Issue 8, June 1, 2025.
The functional asymmetry of the bilateral inhibitory control system in lexical decision task. ABSTRACT Response inhibition control is primarily supported by the right inferior frontal gyrus (IFG) and the prefrontal‐basal ganglia network, though the mechanisms behind right lateralization and regional interplay remain unclear.
Liyue Lin   +7 more
wiley   +1 more source

Caudate nucleus volume in medicated and unmedicated patients with early- and adult-onset schizophrenia. [PDF]

open access: yesSci Rep
Andreou D   +12 more
europepmc   +1 more source

Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline

open access: yesHuman Brain Mapping, Volume 46, Issue 8, June 1, 2025.
We established and validated a machine‐learning model and nomogram in individuals with SCD based on cognitive, sMRI, and fMRI data. Our model achieved 83% accuracy to predict Aβ+ in the brain, with an AUC of 88%, sensitivity of 81%, and specificity of 85% in the test set.
Qinjie Li   +5 more
wiley   +1 more source

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