Titlebook: Machine Learning and Interpretation in Neuroimaging; International Worksh Georg Langs,Irina Rish,Brian Murphy Conference proceedings 2012 S

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Pavan Ramkumar,Sebastian Pannasch,Bruce C. Hansen,Adam M. Larson,Lester C. Loschky

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发牢骚

Population Codes Representing Musical Timbre for High-Level fMRI Categorization of Music Genresa regression model trained on timbral features outperformed models trained on other types of audio features. Our results show that human brain responses to complex, natural music can be differentiated by timbral audio features, emphasizing the importance of timbre in auditory perception.

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CLAY

Categorized EEG Neurofeedback Performance Unveils Simultaneous fMRI Deep Brain ActivationEEG in order to unfold in high spatial and temporal resolutions, respectively the neural modulations induced via T/A EEG-NF. We used real time EEG preprocessing and analysis during the simultaneous T/A EEG-NF/fMRI. A data driven algorithm was implemented off-line to categorize individual scans into

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MKL-Based Sample Enrichment and Customized Outcomes Enable Smaller AD Clinical Trialsfit from a treatment; and . which use an SVM to select a . voxel average for use as an outcome measure. We provide preliminary evidence that these two strategies can lead to significant reductions in sample sizes in hypothetical trials, which directly gives reduced costs and higher efficiency in the

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Conference proceedings 2012itionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light

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