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A Hierarchical Bayesian Mixture Model Approach for Analysis of Resting-State Functional Brain Connectivity : An Alternative to Thresholding

Gorbach, Tetiana, 1991- (author)
Umeå universitet,Statistik,Umeå centrum för funktionell hjärnavbildning (UFBI),Institutionen för integrativ medicinsk biologi (IMB)
Lundquist, Anders, 1978- (author)
Umeå universitet,Umeå centrum för funktionell hjärnavbildning (UFBI),Statistik
de Luna, Xavier, Professor (author)
Umeå universitet,Statistik
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Nyberg, Lars, 1966- (author)
Umeå universitet,Diagnostisk radiologi,Institutionen för integrativ medicinsk biologi (IMB),Umeå centrum för funktionell hjärnavbildning (UFBI)
Salami, Alireza (author)
Karolinska Institutet,Stockholms universitet,Umeå universitet,Diagnostisk radiologi,Umeå centrum för funktionell hjärnavbildning (UFBI),Institutionen för integrativ medicinsk biologi (IMB),Wallenberg centrum för molekylär medicin vid Umeå universitet (WCMM),Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden,Centrum för forskning om äldre och åldrande (ARC), (tills m KI),Umeå University, Sweden
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 (creator_code:org_t)
Mary Ann Liebert, 2020
2020
English.
In: Brain Connectivity. - : Mary Ann Liebert. - 2158-0014 .- 2158-0022. ; 10:5, s. 202-211
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • This article proposes a Bayesian hierarchical mixture model to analyze functional brain connectivity where mixture components represent "positively connected" and "non-connected" brain regions. Such an approach provides a data-informed separation of reliable and spurious connections in contrast to arbitrary thresholding of a connectivity matrix. The hierarchical structure of the model allows simultaneous inferences for the entire population as well as for each individual subject. A new connectivity measure, the posterior probability of a given pair of brain regions of a specific subject to be connected given the observed correlation of regions' activity, can be computed from the model fit. The posterior probability reflects the connectivity of a pair of regions relative to the overall connectivity pattern of an individual, which is overlooked in traditional correlation analyses. This article demonstrates that using the posterior probability might diminish the effect of spurious connections on inferences, which is present when a correlation is used as a connectivity measure. In addition, simulation analyses reveal that the sparsification of the connectivity matrix using the posterior probabilities might outperform the absolute thresholding based on correlations. Therefore, we suggest that posterior probability might be a beneficial measure of connectivity compared with the correlation. The applicability of the introduced method is exemplified by a study of functional resting-state brain connectivity in older adults.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Neurosciences (hsv//eng)
NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Keyword

brain aging
fMRI
functional connectivity
hierarchical modeling
lognormal distribution
resting state

Publication and Content Type

ref (subject category)
art (subject category)

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