[ABE-L] Convite: Seminário DEST/UFMG em 06/08/2021.

[Vinicius Mayrink]

Caros,

Na próxima sexta-feira (*06 de agosto, às 13:30h*) o ciclo de
Seminários do *Departamento
de Estatística da UFMG* terá a apresentação de *Michelle F. Miranda*.

Michelle é Professora do *Department of Mathematics and Statistics* da
*University
of Victoria* (Canadá). Ela fez Bacharelado e Mestrado em Estatística pela
UFMG, e é Doutora em Estatística pela *University of North Carolina*
(Chapel Hill, EUA). Suas linhas de pesquisa são: Inferência Bayesiana,
Machine Learning e Dados de Neuroimagem.

O seminário será transmitido ao vivo pelo canal do Youtube "*Seminários
DEST - UFMG <https://www.youtube.com/channel/UCoZC2_pME9ca_-Hx4djd60w>*":

Att,
Vinícius Mayrink

*********** Título e Resumo ***********

Michelle F. Miranda (University of Victoria, Canadá)

*A computationally scalable Bayesian method for simultaneous detection of
activation signatures and background connectivity for task fMRI data.*

Task-based functional magnetic resonance imaging (fMRI) studies are a
powerful tool to understand human sensory, cognitive, and emotional
processes. To optimally perform a task, the brain enters a task state, and
it needs to maintain it throughout the task. It is hypothesized that this
is done by brain modulation of task-dependent connection patterns. We will
use the term "background connectivity" for the task-dependent modulations
that are due to variations in ongoing brain activity instead of
stimulus-driven activity. We propose a unified modelling approach to
estimate activation signatures and background connectivity in the
working-memory task of the Human Connectome Project. Our model involves a
new hybrid tensor spatial-temporal basis strategy that enables scalable
computing, yet it captures nearby and distant intervoxel correlation and
long-memory temporal correlation.  The spatial basis is a composite hybrid
transform with two levels: the first accounts for within-ROI correlation,
and the second between-ROI distant correlation.  Our basis space model
increases sensitivity for identifying activation signatures, partly driven
by the induced background connectivity that itself can be summarized to
reveal biological insights.

-- 
*Vinícius D. Mayrink*
*Professor Associado - Departamento de Estatística*

*ICEx, Universidade Federal de Minas Gerais*
-------------- Próxima Parte ----------
Um anexo em HTML foi limpo...
URL: <http://lists.ime.usp.br/pipermail/abe/attachments/20210730/1a39aebe/attachment.htm>