Advances in functional and structural MR image analysis and implementation as FSL.

https://cogns.northwestern.edu/cbmg/TBSSNI2006.pdf

Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.

Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.

/pdf/investigations-into-resting-state-connectivity-using-2ztrvae9kb.pdf

Investigations into resting-state connectivity using independent component analysis

A fully probabilistic framework is presented for estimating local probability density functions on parameters of interest in a model of diffusion. This technique is applied to the estimation of parameters in the diffusion tensor model, and also to a simple partial volume model of diffusion. In both cases the parameters of interest include parameters defining local fiber direction. A technique is then presented for using these density functions to estimate global connectivity (i.e., the probability of the existence of a connection through the data field, between any two distant points), allowing for the quantification of belief in tractography results. This technique is then applied to the estimation of the cortical connectivity of the human thalamus. The resulting connectivity distributions correspond well with predictions from invasive tracer methods in nonhuman primate.

/pdf/characterization-and-propagation-of-uncertainty-in-diffusion-22y3bpncsw.pdf

Characterization and propagation of uncertainty in diffusion-weighted MR imaging.

Evidence concerning anatomical connectivities in the human brain is sparse and based largely on limited post-mortem observations. Diffusion tensor imaging has previously been used to define large white-matter tracts in the living human brain, but this technique has had limited success in tracing pathways into gray matter. Here we identified specific connections between human thalamus and cortex using a novel probabilistic tractography algorithm with diffusion imaging data. Classification of thalamic gray matter based on cortical connectivity patterns revealed distinct subregions whose locations correspond to nuclei described previously in histological studies. The connections that we found between thalamus and cortex were similar to those reported for non-human primates and were reproducible between individuals. Our results provide the first quantitative demonstration of reliable inference of anatomical connectivity between human gray matter structures using diffusion data and the first connectivity-based segmentation of gray matter.

Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging

T. E. Behrens, H. Johansen-Berg, I. Drobnjak, J. M. Brady, P. M. Matthews, S. M. Smith, D. J. Higham FMRIB Centre, University of Oxford, United Kingdom, Engineering Science, University of Oxford, United Kingdom, Mathematics, University of Strathclyde, Glasgow, Strathclyde County, United Kingdom Synopsis We aim to explore whether diffusion tractography can be used to define functionally relevant anatomical subunits in the human visual system. Probabilistic diffusion tractography is used to generate connectivity distributions from every voxel in the visual system. A connectivity matrix of the visual system is derived from these distributions and submitted to a spectral reordering routine such that nearby nodes in the reordered matrix are strongly related in the data. The reordered matrix exhibits a hierarchical form in which clusters of nodes emerge. When mapped back into anatomical space, these clusters appear to correspond well with the ventral and dorsal processing streams in the visual system and primary visual cortex. Introduction The anatomical connectivity pattern of a brain region determines its function [1]. Brain areas with distinct functions have different connectivity patterns. Conversely, regions with similar functions have similar connectivity patterns and are highly interconnected. The visual system in the primate brain exhibits both of these characteristics. In simple terms, visual information is sent from primary visual cortex (V1) along 2 broadly parallel processing streams [2] (Figure 1). The ventral pathway processes object form information and includes projections to inferior temporal lobe. The dorsal pathway processes information on object motion and includes projections to posterior parietal cortex. These two processing streams are densely intra-connected, but sparsely inter-connected [3]. Systems that exhibit these distinctive relationships in their connectivity patterns are known as hierarchically connected. A defining feature of such systems is that their connectivity matrices may be reordered (by simultaneous permutation of rows and columns) such that blocks of high connectivity appear around the leading diagonal [4]. Here we use noninvasive imaging of the human brain to provide pairwise connectivity information between all areas of the human visual system and submit these data to a spectral matrix reordering routine to test the hypothesis that they may be organised into a hierarchical form. Diffusion Tractography has previously been used to track major white matter pathways e.g. [5,6] and to define boundaries in grey matter based on connectivity to predefined cortical regions [7]. Here, using only connectivity data derived from probabilistic diffusion tractography [8] we are able to divide the human visual system into anatomically discrete subunits that plausibly correspond to known functional units. Methods Diffusion-weighted data were acquired with an optimised method [9] based on echo planar imaging, implemented on a General Electric 1.5 T Signa Horizon scanner with a standard quadrature head-coil and maximum gradient strength of 22 mT /m. 60 diffusion weighted volumes were acquired with diffusion gradients isotropically distributed in angular space, with a maximum b-value of 1150 smm. Cardiac gating was applied to minimize artefacts due to CSF pulsatile flow. Probabilistic tractography was carried out from each seed voxel in structural space, according to [8]. Connectivity values were stored from each seed voxel to every other voxel in the visual system ( seed/target masks were resampled to a coarse resolution of 10x10x10mm ). Log connectivity information was arranged in a matrix, A. Symmetry was imposed on this matrix by computing C=(A+A)/2 (figure 2 (A) ). The nodes in this matrix were then permuted using a spectral reordering algorithm [10]. This algorithm looks for the node reordering which minimises the sum of element values multiplied by the squared distance of that element from the diagonal, hence forcing large values to the diagonal. The algorithm does not search for clusters in the matrix. Clusters that emerge are due to organisation in the data. Results Figure 2 shows the connectivity matrix in its original form (A) and after spectral reorganisation (B). The matrix was divided by eye into 3 dominant clusters (horizontal coloured bars in Fig 2(b)). These clusters were then mapped back onto their anatomical location (Figure 3). The first cluster (blue) included lateral parietal lobe corresponding to putative dorsal visual stream. The second cluster (yellow) included inferior occipital lobe and inferior temporal lobe corresponding to putative ventral visual stream. The third cluster (red) included medial occipital lobe corresponding to putative primary visual cortex (V1). Clusters are consistent across subjects.

J. M. Brady

Papers

Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging

Delineation of functional subunits in the human cortex from diffusion based connectivity matrices