Restoration of DWI Data Using a Rician LMMSE Estimator

This paper introduces and analyzes a linear minimum mean square error (LMMSE) estimator using a Rician noise model and its recursive version (RLMMSE) for the restoration of diffusion weighted images. A method to estimate the noise level based on local estimations of mean or variance is used to automatically parametrize the estimator. The restoration performance is evaluated using quality indexes and compared to alternative estimation schemes. The overall scheme is simple, robust, fast, and improves estimations. Filtering diffusion weighted magnetic resonance imaging (DW-MRI) with the proposed methodology leads to more accurate tensor estimations. Real and synthetic datasets are analyzed

White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis

Subtle alterations in white matter microstructure are observed in youth at clinical high risk (CHR) for psychosis. However, the timing of these changes and their relationships to the emergence of psychosis remain unclear. Here, we track the evolution of white matter abnormalities in a large, longitudinal cohort of CHR individuals comprising the North American Prodrome Longitudinal Study (NAPLS-3). Multi-shell diffusion magnetic resonance imaging data were collected across multiple timepoints (1–5 over 1 year) in 286 subjects (aged 12–32 years): 25 CHR individuals who transitioned to psychosis (CHR-P; 61 scans), 205 CHR subjects with unknown transition outcome after the 1-year follow-up period (CHR-U; 596 scans), and 56 healthy controls (195 scans). Linear mixed effects models were fitted to infer the impact of age and illness-onset on variation in the fractional anisotropy of cellular tissue (FAT) and the volume fraction of extracellular free water (FW). Baseline measures of white matter microstructure did not differentiate between HC, CHR-U and CHR-P individuals. However, age trajectories differed between the three groups in line with a developmental effect: CHR-P and CHR-U groups displayed higher FAT in adolescence, and 4% lower FAT by 30 years of age compared to controls. Furthermore, older CHR-P subjects (20+ years) displayed 4% higher FW in the forceps major (p < 0.05). Prospective analysis in CHR-P did not reveal a significant impact of illness onset on regional FAT or FW, suggesting that transition to psychosis is not marked by dramatic change in white matter microstructure. Instead, clinical high risk for psychosis—regardless of transition outcome—is characterized by subtle age-related white matter changes that occur in tandem with development

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

Brain Stimulation and Imaging

Noninvasive functional brain stimulation techniques such as transcranial magnetic stimulation (tms) offer the unique possibility of directly interfering with local and remote neural network activity in conscious human participants, with a quantifiable impact on behavior or cognition. This makes brain stimulation in many ways complementary to brain imaging and a combination of both techniques particularly desirable. Brain stimulation can be combined with brain imaging either in two separate experimental sessions or also simultaneously by using tms inside the mr scanner. The simultaneous combination of tms with fmri enables the stimulation of brain circuits while concurrently assessing direct and remote neural network effects across the entire brain and linking these network activity changes to the induced behavioral manipulation. This chapter introduces the workings of tms and its application in fundamental brain research, rehabilitation, and psychiatry and describes the different possibilities and methodological challenges of combining brain stimulation and brain imaging. Concrete research studies are used to exemplify how valuable such combined brain stimulation and brain imaging studies can be for fundamental and clinical brain research

Advanced diffusion imaging for assessing normal white matter development in neonates and characterizing aberrant development in congenital heart disease

Background: Elucidating developmental trajectories of white matter (WM) microstructure is critically important for understanding normal development and regional vulnerabilities in several brain disorders. Diffusion Weighted Imaging (DWI) is currently the method of choice for in-vivo white matter assessment. A majority of neonatal studies use the standard Diffusion Tensor Imaging (DTI) model although more advanced models such as the Neurite Orientation Dispersion and Density Imaging (NODDI) model and the Gaussian Mixture Model (GMM) have been used in adult population. In this study, we compare the ability of these three diffusion models to detect regional white matter maturation in typically developing control (TDC) neonates and regional abnormalities in neonates with congenital heart disease (CHD). Methods: Multiple b-value diffusion Magnetic Resonance Imaging (dMRI) data were acquired from TDC neonates (N = 16) at 38 to 47 gestational weeks (GW) and CHD neonates (N = 19) aged 37 weeks to 41 weeks. Measures calculated from the diffusion signal included not only Mean Diffusivity (MD) and Fractional Anisotropy (FA) derived from the standard DTI model, but also three advanced diffusion measures, namely, the fiber Orientation Dispersion Index (ODI), the isotropic volume fraction (Viso), and the intracellular volume fraction (Vic) derived from the NODDI model. Further, we used two novel measures from a non-parametric GMM, namely the Return-to-Origin Probability (RTOP) and Return-to-Axis Probability (RTAP), which are sensitive to axonal/cellular volume and density respectively. Using atlas-based registration, 22 white matter regions (6 projection, 4 association, and 1 callosal pathways bilaterally in each hemisphere) were selected and the mean value of all 7 measures were calculated in each region. These values were used as dependent variables, with GW as the independent variable in a linear regression model. Finally, we compared CHD and TDC groups on these measures in each ROI after removing age-related trends from both the groups. Results: Linear analysis in the TDC population revealed significant correlations with GW (age) in 12 projection pathways for MD, Vic, RTAP, and 11 pathways for RTOP. Several association pathways were also significantly correlated with GW for MD, Vic, RTAP, and RTOP. The right callosal pathway was significantly correlated with GW for Vic. Consistent with the pathophysiology of altered development in CHD, diffusion measures demonstrated differences in the association pathways involved in language systems, namely the Uncinate Fasciculus (UF), the Inferior Fronto-occipital Fasciculus (IFOF), and the Superior Longitudinal Fasciculus (SLF). Overall, the group comparison between CHD and TDC revealed lower FA, Vic, RTAP, and RTOP for CHD bilaterally in the a) UF, b) Corpus Callosum (CC), and c) Superior Fronto-Occipital Fasciculus (SFOF). Moreover, FA was lower for CHD in the a) left SLF, b) bilateral Anterior Corona Radiata (ACR) and left Retrolenticular part of the Internal Capsule (RIC). Vic was also lower for CHD in the left Posterior Limb of the Internal Capsule (PLIC). ODI was higher for CHD in the left CC. RTAP was lower for CHD in the left IFOF, while RTOP was lower in CHD in the: a) left ACR, b) left IFOF and c) right Anterior Limb of the Internal Capsule (ALIC). Conclusion: In this study, all three methods revealed the expected changes in the WM regions during the early postnatal weeks; however, GMM outperformed DTI and NODDI as it showed significantly larger effect sizes while detecting differences between the TDC and CHD neonates. Future studies based on a larger sample are needed to confirm these results and to explore clinical correlates. Keywords: Neonatal white matter development, Diffusion MRI, Congenital heart diseas

Developmental abnormalities in brain white matter in prodromes with 22q11.2 Deletion Syndrome: A tract based spatial statistics study

Background Schizophrenia is believed to be a neurodevelopmental disorder, and might originate earlier than the first symptoms present clinically. Subjects with 22q11.2 Deletion Syndrome (22q11DS) represent a promising cohort to explore biomarkers of schizophrenia prior to symptoms onset, as there is a 30% incidence of schizophrenia in adult life. In this study, we explored whether changes in whole brain white matter are present in adolescents with 22q11.2DS and in prodromes (22q11DS subjects with a high score on Brief Psychiatric Rating Scale). Methods Diffusion Magnetic Resonance Images (dMRI) of the brain white matter were acquired from 47 controls and 50 subjects with 22q11DS, including 9 prodromes (mean age 18 ± 2 years). Whole brain white matter was analyzed using the Tract Based Spatial Statistics (TBSS) method. dMRI measures, such as Fractional Anisotropy (FA), Mean Diffusivity (MD), Axial Diffusivity (AD) and Radial Diffusivity (RD) were compared between the groups. Results When controls were compared to all the subjects with 22q11DS, statistically significant reductions in MD, AD and RD were found in the 22q11DS group. The changes were localized to the corpus callosum (CC) and the long association fiber tracts. When the 22q11DS group was split, the prodromes showed statistically significant reductions in MD, AD and RD in the CC and Superior Longitudinal Fasciculus (SLF). Discussion Changes in white matter were observed in individuals with 22q11.2DS, which could be interpreted as developmental and axonal abnormalities. The changes found in the prodromes point to even more severe developmental abnormalities. The changes in dMRI indices, reported here, differ from those observed in chronic schizophrenia. In chronic schizophrenia reduced FA and increased RD are being interpreted as abnormalities of the myelin. We hope that studying these prodromes will allow us to develop a more complete understanding of the changes in brain white matter that lead to schizophrenia.N

P.: Brain morphometry by probabilistic latent semantic analysis

Abstract. The paper proposes a new shape morphometry approach to combine advanced classification techniques with geometric features in order to identify morphological abnormalities on brain surface. The overall aim is to improve the classification accuracy in distinguishing between normal subjects and patients affected by Schizophrenia. Being inspired by the approaches for natural language processing, local surface geometric patterns are quantized to visual words, and their co-occurrences are encoded as visual topic. To this aim, a generative model is estimated for each of the two populations by employing the probabilistic Latent Semantic Analysis (pLSA) on shapes. Finally, the generative scores observed for each subject are the input of a Support Vector Machine (SVM), which is properly designed to implement a generative-discriminative classification paradigm. An exhaustive experimental section is proposed on a dataset consisting of MRI scans from 64 patients and 60 control subjects. Promising results are reporting by observing accuracies up to 86.13%.

Small Sample Size Learning for Shape Analysis of Anatomical Structures

We present a novel approach to statistical shape analysis of anatomical structures based on small sample size learning techniques. The high complexity of shape models used in medical image analysis, combined with a typically small number of training examples, places the problem outside the realm of classical statistics. This difficulty is traditionally overcome by first reducing dimensionality of the shape representation (e.g., using PCA) and then performing training and classification in the reduced space defined by a few principal components. We propose to learn the shape differences between the classes in the original high dimensional parameter space, while controlling the capacity (generalization error) of the classifier. This approach makes significantly fewer assumptions on the properties and the distribution of the underlying data, which can be advantageous in anatomical shape analysis where little is known about the true nature of the input data. Support Vector Mach..