Projects:GenerativeBrainConnectivity - Revision history

Polina: /* Publications */

2012-12-10T21:30:38Z

Publications

Pega85 at 21:07, 28 November 2012

2012-11-28T21:07:23Z

Pega85 at 21:05, 28 November 2012

2012-11-28T21:05:10Z

Pega85 at 21:04, 28 November 2012

2012-11-28T21:04:50Z

Pega85 at 21:03, 28 November 2012

2012-11-28T21:03:47Z

Pega85 at 21:02, 28 November 2012

2012-11-28T21:02:43Z

Pega85 at 21:01, 28 November 2012

2012-11-28T21:01:37Z

Pega85 at 20:59, 28 November 2012

2012-11-28T20:59:43Z

Pega85 at 20:27, 28 November 2012

2012-11-28T20:27:27Z

Pega85 at 20:26, 28 November 2012

2012-11-28T20:26:33Z

← Older revision		Revision as of 21:30, 10 December 2012
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	= Publications =		= Publications =
−	[http://www.na-mic.org/publications/pages/display?search=Projects%~~3ABrainConnectivity~~&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked\| NA-MIC Publications Database on Brain Connectivity]	+	[http://www.na-mic.org/publications/pages/display?search=Projects%3AGenerativeBrainConnectivity&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked\| NA-MIC Publications Database on Brain Connectivity]

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 '''Fig 1. (Left) A network model of connectivity. The nodes correspond to regions in the brain, and the lines denote anatomical connections between them. The green nodes and edges are normal. The red nodes are foci of the disease, and the red edges specify pathways of abnormal functional connectivity. The solid lines are deterministic given the region labels; the dashed lines are probabilistic. (Right) Graphical model representation. Vector ''R'' specifies diseased regions. ''A_ij'' and ''F_ij'' represent the latent anatomical and functional connectivity, respectively, between regions ''i'' and ''j''. Variables associated with the diseased population are identified by an overbar. Boxes denote non-random parameters; circles indicate random variables; shaded variables are observed.'''
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 '''Fig 1. (Left) A network model of connectivity. The nodes correspond to regions in the brain, and the lines denote anatomical connections between them. The green nodes and edges are normal. The red nodes are foci of the disease, and the red edges specify pathways of abnormal functional connectivity. The solid lines are deterministic given the region labels; the dashed lines are probabilistic. (Right) Graphical model representation. Vector ''R'' specifies diseased regions. ''A_ij'' and ''F_ij'' represent the latent anatomical and functional connectivity, respectively, between regions ''i'' and ''j''. Variables associated with the diseased population are identified by an overbar. Boxes denote non-random parameters; circles indicate random variables; shaded variables are observed.'''
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 '''Fig 1. (Left) A network model of connectivity. The nodes correspond to regions in the brain, and the lines denote anatomical connections between them. The green nodes and edges are normal. The red nodes are foci of the disease, and the red edges specify pathways of abnormal functional connectivity. The solid lines are deterministic given the region labels; the dashed lines are probabilistic. (Right) Graphical model representation. Vector ''R'' specifies diseased regions. ''A_ij'' and ''F_ij'' represent the latent anatomical and functional connectivity, respectively, between regions ''i'' and ''j''. Variables associated with the diseased population are identified by an overbar. Boxes denote non-random parameters; circles indicate random variables; shaded variables are observed.'''
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 We segmented the structural images into 77 anatomical regions with Freesurfer. The DWI data is analyzed using a two-tensor tractography algorithm. The connectivity measure is the average FA along all detected fibers between regions. The measure is set to zero if no tracts are found. We compute the fMRI connectivity as the Pearson correlation coefficient between the mean time courses of the two regions.
-'''Fig 2. Significant anatomical and functional connectivity differences. Blue lines indicate higher connectivity in the control group; yellow lines indicate higher connectivity in the schizophrenia population.'''
+'''Fig 2. Significant regions based on permutation tests. The colorbar corresponds to the negative log p-value. We present the lateral and medial viewpoints for each hemisphere. The highlighted regions are the posterior cingulate (R PCC) and the superior temporal gyrus (L STG & R STG).'''
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 = Joint Modeling of Anatomical and Functional Connectivity for Population Studies =
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 The interaction between functional and anatomical connectivity provides a rich framework for understanding the brain. Functional connectivity is commonly measured via temporal correlations in resting-state fMRI data. These correlations are believed to reflect the intrinsic functional organization of the brain. Anatomical connectivity is often measured using DWI tractography, which estimates the configuration of underlying white matter fibers. In this work we propose and demonstrate a novel probabilistic framework to infer the relationship between these modalities. The model is based on ''latent'' connectivities between brain regions and makes intuitive assumptions about the data generation process. We present a natural extension of the model to population studies, which we use to identify widespread connectivity changes in schizophrenia.
-'''Fig 1. Joint model for the effects of schizophrenia. The pairwise connections are indexed with ''n=1,...,N''.  ''A_n'' represents the latent anatomical connectivity of the ''nth'' connection in the control template, and ''F_n'' denotes the corresponding latent functional connectivity. ''D_nj'' and ''B_nj'' are the observed DWI and fMRI measurements, respectively, for the ''nth'' connection in the ''jth'' subject in the control population. The schizophrenia templates are identified by an overbar, and the subjects are indexed by ''m=1,\ldots,M''. Squares indicate non-random parameters; circles indicate random variables; observed variables are shaded.'''
+'''Fig 4. Joint model for the effects of schizophrenia. The pairwise connections are indexed with ''n=1,...,N''.  ''A_n'' represents the latent anatomical connectivity of the ''nth'' connection in the control template, and ''F_n'' denotes the corresponding latent functional connectivity. ''D_nj'' and ''B_nj'' are the observed DWI and fMRI measurements, respectively, for the ''nth'' connection in the ''jth'' subject in the control population. The schizophrenia templates are identified by an overbar, and the subjects are indexed by ''m=1,\ldots,M''. Squares indicate non-random parameters; circles indicate random variables; observed variables are shaded.'''
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-Unlike voxel- and ROI-based analysis, we model the behavior of pairwise connections between regions of the brain. Fig. 1 depicts our generative model. We define two latent variables: ''anatomical connectivity'' is a binary random variable that indicates the presence or absence of a direct anatomical pathway between the regions. ''Functional connectivity'' is a tri-state random variable that represent little or no functional co-activation, positive functional synchrony, and negative functional synchrony between the regions. These variables specify templates for each (control, schizophrenia) population. Our observed variables are correlations in resting-state fMRI and average FA values along the white matter tracts. We model differences between the groups within the latent connectivities alone and share the data likelihood distributions between the two populations.
+Unlike voxel- and ROI-based analysis, we model the behavior of pairwise connections between regions of the brain. Fig. 4 depicts our generative model. We define two latent variables: ''anatomical connectivity'' is a binary random variable that indicates the presence or absence of a direct anatomical pathway between the regions. ''Functional connectivity'' is a tri-state random variable that represent little or no functional co-activation, positive functional synchrony, and negative functional synchrony between the regions. These variables specify templates for each (control, schizophrenia) population. Our observed variables are correlations in resting-state fMRI and average FA values along the white matter tracts. We model differences between the groups within the latent connectivities alone and share the data likelihood distributions between the two populations.
 ''' ''Experimental Results'' '''
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 We segmented the structural images into 77 anatomical regions with Freesurfer. To inject prior clinical knowledge, we pre-selected 8 brain structures (corresponding to 16 regions) that are believed to play a role in schizophrenia: the superior temporal gyrus, rostral middle frontal gyrus, hippocampus, amygdala, posterior cingulate, rostral anterior cingulate, parahippocampal gyrus, and transverse temporal gyrus. We model only the 1096 unique pairwise connections between these ROIs and all other regions in the brain.
-'''Fig 2. Significant anatomical and functional connectivity differences. Blue lines indicate higher connectivity in the control group; yellow lines indicate higher connectivity in the schizophrenia population.'''
+'''Fig 5. Significant anatomical and functional connectivity differences. Blue lines indicate higher connectivity in the control group; yellow lines indicate higher connectivity in the schizophrenia population.'''
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-Fig. 2 depicts the significantly different anatomical and functional connections identified by the algorithm.  As seen, schizophrenia patients exhibit increased functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe. These results confirm the hypotheses of widespread functional connectivity changes in schizophrenia and of functional abnormalities involving the default network.
+Fig. 5 depicts the significantly different anatomical and functional connections identified by the algorithm.  As seen, schizophrenia patients exhibit increased functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe. These results confirm the hypotheses of widespread functional connectivity changes in schizophrenia and of functional abnormalities involving the default network.
 The differences in anatomical connectivity implicate the superior temporal gyrus and hippocampus. We note that relatively few anatomical connections exhibit significant differences between the two populations. This may stem from our choice of ROIs. In particular, we rely on Freesurfer parcellations, which provide anatomically meaningful correspondences across subjects and mitigate the effects of minor registration errors. However, they may be too big to capture structural differences between the groups. We emphasize that our model can be easily applied to finer scale parcelations in future studies.