USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR

We present USLR, a computational framework for longitudinal registration of brain MRI scans to estimate nonlinear image trajectories that are smooth across time, unbiased to any timepoint, and robust to imaging artefacts. It operates on the Lie algebra parameterisation of spatial transforms (which is compatible with rigid transforms and stationary velocity fields for nonlinear deformation) and takes advantage of log-domain properties to solve the problem using Bayesian inference. USRL estimates rigid and nonlinear registrations that: (i) bring all timepoints to an unbiased subject-specific space; and (i) compute a smooth trajectory across the imaging time-series. We capitalise on learning-based registration algorithms and closed-form expressions for fast inference. A use-case Alzheimer's disease study is used to showcase the benefits of the pipeline in multiple fronts, such as time-consistent image segmentation to reduce intra-subject variability, subject-specific prediction or population analysis using tensor-based morphometry. We demonstrate that such approach improves upon cross-sectional methods in identifying group differences, which can be helpful in detecting more subtle atrophy levels or in reducing sample sizes in clinical trials. The code is publicly available in https://github.com/acasamitjana/uslrComment: Submitted to Medical Image Analysi

Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination.

The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have been few assessments of their differences, making it difficult to choose approaches and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled dataset of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p<0.05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high-dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA-based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC

3D Face Recognition using Sparse Spherical Representations

This paper addresses the problem of 3D face recognition using spherical sparse representations. We first propose a fully automated registration process that permits to align the 3D face point clouds. These point clouds are then represented as signals on the 2D sphere, in order to take benefit of the geometry information. Simultaneous sparse approximations implement a dimensionality reduction process by subspace projection. Each face is typically represented by a few spherical basis functions that are able to capture the salient facial characteristics. The dimensionality reduction step preserves the discriminant facial information and eventually permits an effective matching in the reduced space, where it can further be combined with LDA for improved recognition performance. We evaluate the 3D face recognition algorithm on the FRGC v.1.0 data set, where it outperforms classical state-of-the-art solutions based on PCA or LDA on depth face images

Reorganization of brain networks in aging: a review of functional connectivity studies

Healthy aging (HA) is associated with certain declines in cognitive functions, even in individuals that are free of any process of degenerative illness. Functional magnetic resonance imaging (fMRI) has been widely used in order to link this age-related cognitive decline with patterns of altered brain function. A consistent finding in the fMRI literature is that healthy old adults present higher activity levels in some brain regions during the performance of cognitive tasks. This finding is usually interpreted as a compensatory mechanism. More recent approaches have focused on the study of functional connectivity, mainly derived from resting state fMRI, and have concluded that the higher levels of activity coexist with disrupted connectivity. In this review, we aim to provide a state-of-the-art description of the usefulness and the interpretations of functional brain connectivity in the context of HA. We first give a background that includes some basic aspects and methodological issues regarding functional connectivity. We summarize the main findings and the cognitive models that have been derived from task-activity studies, and we then review the findings provided by resting-state functional connectivity in HA. Finally, we suggest some future directions in this field of research. A common finding of the studies included is that older subjects present reduced functional connectivity compared to young adults. This reduced connectivity affects the main brain networks and explains age-related cognitive alterations. Remarkably, the default mode network appears as a highly compromised system in HA. Overall, the scenario given by both activity and connectivity studies also suggests that the trajectory of changes during task may differ from those observed during resting-state. We propose that the use of complex modeling approaches studying effective connectivity may help to understand context-dependent functional reorganizations in the aging process

Resting state networks in the TgF344-AD rat model of Alzheimer's Disease are altered from early stages

A better and non-invasive characterization of the preclinical phases of Alzheimer's disease (AD) is important to advance its diagnosis and obtain more effective benefits from potential treatments. The TgF344-AD rat model has been well characterized and shows molecular, behavioral and brain connectivity alterations that resemble the silent period of the pathology. Our aim was to longitudinally investigate functional brain connectivity in established resting-state networks (RSNs) obtained by independent component analysis (ICA) in a cohort of TgF344-AD and control rats every 3 months, from 5 to 18 months of age, to cover different stages of the disease. Before each acquisition, working memory performance was evaluated by the delayed non match-to-sample (DNMS) task. Differences in the temporal evolution were observed between groups in the amplitude and shape of the somatosensorial and sensorimotor networks but not in the whole default mode network (DMN). Subsequent high dimensional ICA analysis showed early alterations in the anterior DMN subnetwork activity of TgF344-AD rats compared to controls. Performance of DNMS task was positively correlated with somatosensorial network at 5 months of age in the wild-type animals but not in the Tg-F344 rats. At different time points, DMN showed negative correlation with cognitive performance in the control group while in the transgenic group the correlation was positive. In addition, behavioral differences observed at 5 months of age correlated with alterations in the posterior DMN subnetwork. We have demonstrated that functional connectivity using ICA represents a useful biomarker also in animal models of AD such as the TgF344AD rats, as it allows the identification of alterations associated with the progression of the disease, detecting differences in specific networks even at very early stages

Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimers disease

Linear mixed effects (LME) modelling under both frequentist and Bayesian frameworks can be used to study longitudinal trajectories. We studied the performance of both frameworks on different dataset configurations using hippocampal volumes from longitudinal MRI data across groups-healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients, including subjects that converted from MCI to AD. We started from a big database of 1250 subjects from the Alzheimer's disease neuroimaging initiative (ADNI), and we created different reduced datasets simulating real-life situations using a random-removal permutation-based approach. The number of subjects needed to differentiate groups and to detect conversion to AD was 147 and 115 respectively. The Bayesian approach allowed estimating the LME model even with very sparse databases, with high number of missing points, which was not possible with the frequentist approach. Our results indicate that the frequentist approach is computationally simpler, but it fails in modelling data with high number of missing values

White matter integrity related to functional working memory networks in traumatic brain injury

Objective: This study explores the functional and structural patterns of connectivity underlying working memory impairment after severe traumatic axonal injury. Methods: We performed an fMRI n-back task and acquired diffusion tensor images (DTI) in a group of 19 chronic-stage patients with severe traumatic brain injury (TBI) and evidence of traumatic axonal injury and 19 matched healthy controls. We performed image analyses with FSL software and fMRI data were analyzed using probabilistic independent component analysis. Fractional anisotropy (FA) maps from DTI images were analyzed with FMRIB's Diffusion Toolbox. Results: We identified working memory and default mode networks. Global FA values correlated with both networks and FA whole-brain analysis revealed correlations in several tracts associated with the functional activation. Furthermore, working memory performance in the patient group correlated with the functional activation patterns and with the FA values of the associative fasciculi. Conclusion: Combining structural and functional neuroimaging data, we were able to describe structural white matter changes related to functional network alterations and to lower performance in working memory in chronic TBI

Cortical thickness and behavior abnormalities in children born preterm

Abstract Aim To identify long-term effects of preterm birth and of periventricular leukomalacia (PVL) on cortical thickness (CTh). To study the relationship between CTh and cognitive-behavioral abnormalities. Methods We performed brain magnetic resonance imaging on 22 preterm children with PVL, 14 preterm children with no evidence of PVL and 22 full-term peers. T1-weighted images were analyzed with FreeSurfer software. All participants underwent cognitive and behavioral assessments by means of the Wechsler Intelligence Scales for Children-Fourth Edition (WISC-IV) and the Child Behavior Checklist (CBCL). Results We did not find global CTh differences between the groups. However, a thinner cortex was found in left postcentral, supramarginal, and caudal middle rostral gyri in preterm children with no evidence of PVL than in the full-term controls, while PVL preterm children showed thicker cortex in right pericalcarine and left rostral middle frontal areas than in preterm children with no evidence of PVL. In the PVL group, internalizing and externalizing scores correlated mainly with CTh in frontal areas. Attentional scores were found to be higher in PVL and correlated with CTh increments in right frontal areas. Interpretation The preterm group with no evidence of PVL, when compared with full-term children, showed evidence of a different pattern of regional thinning in the cortical gray matter. In turn, PVL preterm children exhibited atypical increases in CTh that may underlie their prevalent behavioral problems

White Matter/Gray Matter Contrast Changes in Chronic and Diffuse Traumatic brain Injury

Signal-intensity contrast of T1-weighted magnetic resonance imaging scans has been associated with tissue integrity and reported as a sign of neurodegenerative changes in diseases such as Alzheimer's disease. After severe traumatic brain injury (TBI), progressive structural changes occur in white (WM) and gray matter (GM). In the current study, we assessed the signal-intensity contrast of GM and WM in patients with diffuse TBI in the chronic stage to (1) characterize the regional pattern of WM/GM changes in intensity contrast associated with traumatic axonal injury, (2) evaluate possible associations between this measure and diffusion tensor image (DTI)/fractional anisotropy (FA) for detecting WM damage, and (3) investigate the correlates of both measures with cognitive outcomes. Structural T1 scans were processed with FreeSurfer software to identify the boundary and calculate the WM/GM contrast maps. DTIs were processed with the FMRIB software library to obtain FA maps. The WM/GM contrast in TBI patients showed a pattern of reduction in almost all of the brain, except the visual and motor primary regions. Global FA values obtained from DTI correlated with the intensity contrast of all associative cerebral regions. WM/GM contrast correlated with memory functions, whereas FA global values correlated with tests measuring memory and mental processing speed. In conclusion, tissue-contrast intensity is a very sensitive measure for detecting structural brain damage in chronic, severe and diffuse TBI, but is less sensitive than FA for reflecting neuropsychological sequelae, such as impaired mental processing speed

Functional brain networks and cognitive deficits in Parkinson's disease

Abstract: Graph-theoretical analyses of functional networks obtained with resting-state functional mag-netic resonance imaging (fMRI) have recently proven to be a useful approach for the study of the sub-strates underlying cognitive deficits in different diseases. We used this technique to investigate whethercognitive deficits in Parkinson's disease (PD) are associated with changes in global and local networkmeasures. Thirty-six healthy controls (HC) and 66 PD patients matched for age, sex, and education wereclassified as having mild cognitive impairment (MCI) or not based on performance in the three mainlyaffected cognitive domains in PD: attention/executive, visuospatial/visuoperceptual (VS/VP), anddeclarative memory. Resting-state fMRI and graph theory analyses were used to evaluate network meas-ures. We have found that patients with MCI had connectivity reductions predominantly affecting long-range connections as well as increased local interconnectedness manifested as higher measures of cluster-ing, small-worldness, and modularity. The latter measures also tended to correlate negatively with cogni-tive performance in VS/VP and memory functions. Hub structure was also reorganized: normal hubsdisplayed reduced centrality and degree in MCI PD patients. Our study indicates that the topologicalproperties of brain networks are changed in PD patients with cognitive deficits. Our findings providenovel data regarding the functional substrate of cognitive impairment in PD, which may prove to havevalue as a prognostic marker