Gray and white matter correlates of navigational ability in humans.

Humans differ widely in their navigational abilities. Studies have shown that self-reports on navigational abilities are good predictors of performance on navigation tasks in real and virtual environments. The caudate nucleus and medial temporal lobe regions have been suggested to subserve different navigational strategies. The ability to use different strategies might underlie navigational ability differences. This study examines the anatomical correlates of self-reported navigational ability in both gray and white matter. Local gray matter volume was compared between a group (N = 134) of good and bad navigators using voxel-based morphometry (VBM), as well as regional volumes. To compare between good and bad navigators, we also measured white matter anatomy using diffusion tensor imaging (DTI) and looked at fractional anisotropy (FA) values. We observed a trend toward higher local GM volume in right anterior parahippocampal/rhinal cortex for good versus bad navigators. Good male navigators showed significantly higher local GM volume in right hippocampus than bad male navigators. Conversely, bad navigators showed increased FA values in the internal capsule, the white matter bundle closest to the caudate nucleus and a trend toward higher local GM volume in the caudate nucleus. Furthermore, caudate nucleus regional volume correlated negatively with navigational ability. These convergent findings across imaging modalities are in line with findings showing that the caudate nucleus and the medial temporal lobes are involved in different wayfinding strategies. Our study is the first to show a link between self-reported large-scale navigational abilities and different measures of brain anatomy.

An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease.

Understanding the progression of neurological diseases is vital for accurate and early diagnosis and treatment planning. We introduce a new characterization of disease progression, which describes the disease as a series of events, each comprising a significant change in patient state. We provide novel algorithms to learn the event ordering from heterogeneous measurements over a whole patient cohort and demonstrate using combined imaging and clinical data from familial Alzheimer's and Huntington's disease cohorts. Results provide new detail in the progression pattern of these diseases, while confirming known features, and give unique insight into the variability of progression over the cohort. The key advantage of the new model and algorithms over previous progression models is that they do not require a priori division of the patients into clinical stages. The model and its formulation extend naturally to a wide range of other diseases and developmental processes and accommodate cross-sectional and longitudinal input data.

An event-based disease progression model and its application to familial Alzheimer's disease.

This study introduces a novel event-based model for disease progression. The model describes disease progression as a series of events. An event can consist of a significant change in symptoms or in tissue. We construct a forward model that relates heterogeneous measurements from a whole cohort of patients and controls to the event sequence and fit the model with a Bayesian estimation framework. The model does not rely on a priori classification of patients and therefore has the potential to describe disease progression in much greater detail than previous approaches. We demonstrate our model on serial T1 MRI data from a familial Alzheimer's disease cohort. We show progression of neuronal atrophy on a much finer level than previous studies, while confirming progression patterns from pathological studies, and integrate clinical events into the model.

Topographical functional connectivity pattern in the perisylvian language networks.

We performed a resting-state functional connectivity study to investigate directly the functional correlations within the perisylvian language networks by seeding from 3 subregions of Broca's complex (pars opercularis, pars triangularis, and pars orbitalis) and their right hemisphere homologues. A clear topographical functional connectivity pattern in the left middle frontal, parietal, and temporal areas was revealed for the 3 left seeds. This is the first demonstration that a functional connectivity topology can be observed in the perisylvian language networks. The results support the assumption of the functional division for phonology, syntax, and semantics of Broca's complex as proposed by the memory, unification, and control (MUC) model and indicated a topographical functional organization in the perisylvian language networks, which suggests a possible division of labor for phonological, syntactic, and semantic function in the left frontal, parietal, and temporal areas.

Exploring the anatomical basis of effective connectivity models with DTI-based fiber tractography.

Diffusion tensor imaging (DTI) is considered to be a promising tool for revealing the anatomical basis of functional networks. In this study, we investigate the potential of DTI to provide the anatomical basis of paths that are used in studies of effective connectivity, using structural equation modeling. We have taken regions of interest from eight previously published studies, and examined the connectivity as defined by DTI-based fiber tractography between these regions. The resulting fiber tracts were then compared with the paths proposed in the original studies. For a substantial number of connections, we found fiber tracts that corresponded to the proposed paths. More importantly, we have also identified a number of cases in which tractography suggested direct connections which were not included in the original analyses. We therefore conclude that DTI-based fiber tractography can be a valuable tool to study the anatomical basis of functional networks.

Probabilistic inference on Q-ball imaging data.

Diffusion-weighted magnetic resonance imaging MRI) and especially diffusion tensor imaging (DTI) have proven to be useful for the characterization of the microstructure of brain white matter structures in vivo. However, DTI suffers from a number of limitations in characterizing more complex situations. The most notable problem occurs when multiple fibre bundles are present within a voxel. In this paper, we have expanded the existing Q-ball imaging method to a Bayesian framework in order to fully characterize the uncertainty around the fibre directions, given the quality of the data. We have done this by using a recently proposed spherical harmonics decomposition of the diffusion-weighted signal and the resulting Q-ball orientation distribution function. Moreover, we have incorporated a model selection procedure which determines the appropriate smoothness of the orientation distribution function from the data. We show by simulation that our framework can indeed characterize the posterior probability of the fibre directions in cases with multiple fibre populations per voxel and have provided examples of the algorithm's performance on real data where this situation is known to occur.