Agreement between the white matter connectivity based on the tensor-based morphometry and the volumetric white matter parcellations based on diffusion tensor imaging

We are interested in investigating white matter connectivity using a novel computational framework that does not use diffusion tensor imaging (DTI) but only uses T1-weighted magnetic resonance imaging. The proposed method relies on correlating Jacobian determinants across different voxels based on the tensor-based morphometry (TBM) framework. In this paper, we show agreement between the TBM-based white matter connectivity and the DTI-based white matter atlas. As an application, altered white matter connectivity in a clinical population is determined

Morphometric and connectivity white matter abnormalities in Obsessive Compulsive Disorder

Two psychological mechanisms seem to be associated with the obsessive-compulsive cycle: (1) an emotional mechanism characterized by intense emotional arousal associated with intrusive thoughts of impending danger; (2) a cognitive mechanism exemplified by difficulties with inhibitory control. Several studies found more extensive cognitive deficits in Obsessive Compulsive Disorder (OCD) beyond problems of inhibitory control and emotional regulation, namely: visual-spatial processing and memory. Thus, there is now extensive research showing that alterations of these psychological mechanisms in OCD (i.e., inhibitory control, emotional regulation, working memory, and visual spatial processing) are associated with morphological gray matter alterations in widespread brain regions. More recently, researchers have started looking at white matter abnormalities in OCD. In this article we review the research looking at white matter morphometric and structural connectivity alterations in OCD. Altogether, while some contradictory findings are still present, there is now evidence for widespread white matter morphometric and connectivity abnormalities affecting major white matter tracts (superior longitudinal fasciculus, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, cingulum bundle, semioval center, internal capsule, different regions of the corpus callosum, thalamic radiation, uncinate fasciculus and optic radiation) as well as white matter in regions adjacent to gray matter structures (superior frontal gyrus, dorsolateral prefrontal medial frontal cortex; inferior frontal gyrus, caudate, insulate cortex, parietal cortex, supramarginal and lingual gyri, and thalamus). These white matter alterations may help explaining the diversity of OCD psychological impairments in inhibitory control, emotional regulation, memory and visual spatial processing.The authors have no financial or personal conflicts of interest. The first author was funded by the Brazilian National Counsel for Scientific and Technological Development (CNPq) as a Special Visiting Researcher of the Science Without Borders program (grant number: 401143/2014-7). This study was partially conducted at the Neuropsychophysiology Lab from the Psychology Research Centre (UID/PSI/01662/2013), University of Minho, and supported by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Science, Technology and Higher Education through national funds and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145- FEDER-007653).info:eu-repo/semantics/publishedVersio

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography

Recent studies combining difusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson's disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classifcation performance of subcortical FA and MD was also evaluated to compare the discriminant ability between difusion tensor-derived metrics and NOS. Using difusion-weighted images acquired in a 3T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classifcation procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classifed. NOS features outperformed the discrimination performance obtained with FA and MD. Our fndings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than difusion tensor-derived metrics for the detection of MSA

Atypical Development of Broca’s Area in a Large Family with Inherited Stuttering

Developmental stuttering is a condition of speech dysfluency, characterised by pauses, blocks, prolongations, and sound or syllable repetitions. It affects around 1% of the population, with potential detrimental effects on mental health and long-term employment. Accumulating evidence points to a genetic aetiology, yet gene-brain associations remain poorly understood due to a lack of MRI studies in affected families. Here we report the first neuroimaging study of developmental stuttering in a family with autosomal dominant inheritance of persistent stuttering. We studied a four-generation family, sixteen family members were included in genotyping analysis. T1-weighted and diffusion weighted MRI scans were conducted on seven family members (6 male; aged 9–63 years) with two age and sex matched controls without stuttering (N = 14). Using Freesurfer, we analysed cortical morphology (cortical thickness, surface area and local gyrification index) and basal ganglia volumes. White matter integrity in key speech and language tracts (i.e. frontal aslant tract and arcuate fasciculus) was also analysed using MRtrix and probabilistic tractography. We identified a significant age by group interaction effect for cortical thickness in the left hemisphere pars opercularis (Broca’s area). In affected family members this region failed to follow the typical trajectory of age-related thinning observed in controls. Surface area analysis revealed the middle frontal gyrus region was reduced bilaterally in the family (all cortical morphometry significance levels set at a vertex-wise threshold of p < 0.01, corrected for multiple comparisons). Both the left and right globus pallidus were larger in the family than in the control group (left p = 0.017; right p=0.037), and a larger right globus pallidus was associated with more severe stuttering (rho =0.86, p=0.01). No white matter differences were identified. Genotyping identified novel loci on chromosomes 1 and 4 that map with the stuttering phenotype. Our findings denote disruption within the cortico-basal ganglia-thalamo-cortical network. The lack of typical development of these structures reflects the anatomical basis of the abnormal inhibitory control network between Broca’s area and the striatum underpinning stuttering in these individuals. This is the first evidence of a neural phenotype in a family with an autosomal dominantly inherited stuttering

Disrupted structural connectivity of fronto-deep gray matter pathways in progressive supranuclear palsy

Background: Structural connectivity is a promising methodology to detect patterns of neural network dysfunction in neurodegenerative diseases. This approach has not been tested in progressive supranuclear palsy (PSP). Objectives: The aim of this study is reconstructing the structural connectome to characterize and detect the pathways of degeneration in PSP patients compared with healthy controls and their correlation with clinical features. The second objective is to assess the potential of structural connectivity measures to distinguish between PSP patients and healthy controls at the single-subject level. Methods: Twenty healthy controls and 19 PSP patients underwent diffusion-weighted MRI with a 3T scanner. Structural connectivity, represented by number of streamlines, was derived from probabilistic tractography. Global and local network metrics were calculated based on graph theory. Results: Reduced numbers of streamlines were predominantly found in connections between frontal areas and deep gray matter (DGM) structures in PSP compared with controls. Significant changes in structural connectivity correlated with clinical features in PSP patients. An abnormal small-world architecture was detected in the subnetwork comprising the frontal lobe and DGM structures in PSP patients. The classification procedure achieved an overall accuracy of 82.23% with 94.74% sensitivity and 70% specificity. Conclusion: Our findings suggest that modelling the brain as a structural connectome is a useful method to detect changes in the organization and topology of white matter tracts in PSP patients. Secondly, measures of structural connectivity have the potential to correctly discriminate between PSP patients and healthy controls

Brain Micro- and Macro-Structural Characteristics Investigation in Fibromyalgia Using Multi-Modal Magnetic Resonance Imaging

Fibromyalgia (FM) is a chronic widespread pain condition that deeply impacts the lives of patients. Multiple symptoms such as fatigue, impaired cognition, and sleep disturbances among others are commonly described. Despite intensive research effort, no disease-specific mechanism uniquely explains the clinical presentation of FM. Nonetheless, current evidence points to a major role of the central nervous system for the main feature of this condition: pain and sensory augmentation. Neuroimaging techniques provide a window into the brain mechanisms that may play a role in FM. Several studies using functional magnetic resonance imaging (MRI) show abnormalities in pain processing in the brain of FM patients. Likewise, structural abnormalities are found using anatomical MRI however the findings are less consistent. The main goal of this dissertation was to comprehensively assess brain structural features of FM patients and matched controls at both micro- (cellular-level structures such as membranes, myelin as well as axonal density) and macro-structural (gross anatomical) levels as measured by diffusion-weighted and high-resolution anatomical MRI respectively. The results from diffusion MRI show evidence of widespread micro-structural white matter (WM) abnormalities in the brains of FM patients compared to controls, and also within relevant pain-related brain regions. These findings give support to the view that alterations in the brain of patients potentially contribute to the symptoms experienced by them. Conversely, macro-structural brain features showed little difference between patients and controls regarding gray matter (GM) characteristics. Between-group differences were only found for increased volume in the amygdalae and WM adjacent to the anterior cingulate cortex and left insula for FM patients relative to controls. Taken together these findings may indicate that structural abnormalities in the brain of FM patients are more widespread in the micro-structural level, while regional differences limited to subcortical structures and WM adjacent to pain-related cortical areas are more typical at the macro-structural level with no measurable impact to GM morphological characteristics.Doctor of Philosoph

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease

The white matter query language: a novel approach for describing human white matter anatomy

International audienceWe have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI vol¬umes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroanatomist's expert knowledge. The framework is based on a novel query language with a near-to-English textual syntax. This query language makes it possible to construct a dictionary of anatomical definitions that describe white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This novel method makes it possible to automatically label white matter anatomy across subjects. After describing this method, we provide an example of its implementation where we encode anatomical knowledge in human white matter for 10 association and 15 projection tracts per hemisphere, along with 7 commissural tracts. Importantly, this novel method is comparable in accuracy to manual labeling. Finally, we present results applying this method to create a white matter atlas from 77 healthy subjects, and we use this atlas in a small proof-of-concept study to detect changes in association tracts that characterize schizophrenia

Methods for improved mapping of brain lesion connectivity

Recent advances over the past two decades in neuroimaging methods have enabled us to map the connectivity of the brain. In parallel, pathophysiological models of brain disease have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disruptions to interconnected neural networks. Nevertheless, these recent methods for mapping brain connectivity are still under development. Every step of the mapping process becomes a potential source for additional error due to noise or artifacts that could impact final analyses. Segmentation, parcellation, registration, and tractography are some of the steps where this occurs. Moreover, mapping the connectivity in a brain lesion is even more susceptible to errors in these steps. In this body of work, I describe multiple new methods for improving the accuracy of mapping lesion connectivity by reducing errors at the tractography stage which is the most error prone stage. First, we develop an approach for directly normalizing streamlines into a template space that avoids performing tractography in the normalized template space, reducing the error of connectomes constructed in the template space with respect to the ground truth native space connectome. Second, we develop a rapid approach for performing shortest path tractography and constructing shortest path probability weighted connectomes which increases the connection specificity relative to local streamline tracking approaches. We then demonstrate how our shortest path tractography approach can be used construct a disconnectome, a connectivity map of the proportion of connections lost due to intersecting a lesion. We then develop a fast, greedy graph-theoretic algorithm that extracts the maximally disconnected subgraph containing brain regions with the greatest shared loss of connectivity. Finally, we demonstrate how combining methods from diffusion based image inpainting and optimal estimation can be used to restore or inpaint corrupted fiber diffusion models in lesioned white matter tissue, enabling tractography and the study of lesion connectivity and modeling of microstructural measures in the patient’s native space