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Biology subjects

Laymon, C.

Publications and source records attributed to Laymon, C..

2 recordsLinked to original sources

Striatal dopamine supports reward reactivity and learning: A simultaneous PET/fMRI study

Converging evidence from both human neuroimaging and animal studies has supported a model of mesolimbic processing in computing prediction errors, which form the basis of reward learning behaviors. However, direct evidence demonstrating how human dopamine signaling in the basal ganglia contributes to learning has been hampered by limitations of individual imaging modalities. Here, we present data from a large (N=81, 18-30 year olds), multi-modal neuroimaging study using simultaneously acquired task fMRI, affording temporal resolution of reward system function, and PET imaging with [11C]Raclopride (RAC) assessing striatal D2/3 receptor binding. Results indicated that task-related dopamine release in the ventral striatum, measured as a change in RAC binding, was greater among those who demonstrated successful reward learning on a probabilistic map learning task. This learning response was specific to the ventral striatum and was not present in fMRI BOLD reward response activation. This provides support for considering task-related DA release in ventral striatum as a key signal for translating reward outcomes into a learning signal, rather the representing the reward outcome in isolation. These data provide novel, human in vivo evidence that dopaminergic function may support reward reactivity as well as reward learning as distinct processes.

neuroscience↗

Multimodal magnetic resonance imaging predicts regional amyloid-β burden in the brain

Alzheimers disease (AD) is the most common cause of dementia and identifying early markers of this disease is important for prevention and treatment strategies. Amyloid - {beta} protein deposition is one of the earliest detectable pathological changes in AD. But in-vivo detection of amyloid - {beta} using positron emission tomography (PET) is hampered by high cost and limited geographical accessibility. These factors can become limiting when PET is used to screen large numbers of subjects into prevention trials when only a minority are expected to be amyloid- {beta} - positive. Structural MRI is advantageous; as it is relatively inexpensive and more accessible. Thus it could be widely used in large studies, even when frequent or repetitive imaging is necessary. We used a machine learning, pattern recognition, approach using intensity-based features from individual and combination of MR modalities (T1 weighted, T2 weighted, T2 fluid attenuated inversion recovery [FLAIR], susceptibility weighted imaging) to predict voxel-level amyloid- {beta} in the brain. The MR- amyloid {beta} relation was learned within each subject and generalized across subjects using subject-specific features (demographic, clinical, and summary MR features). When compared to other modalities, combination of T1-weighted, T2-weighted FLAIR, and SWI performed best in predicting the amyloid- {beta} status as positive or negative. T2- weighted performed the best in predicting change in amyloid- {beta} over two timepoints. Overall, our results show feasibility of amyloid- {beta} prediction by MRI.

bioengineering↗