Virtual in vivo interactive dissection of white matter fasciculi in the human brain.

This work reports the use of diffusion tensor magnetic resonance tractography to visualize the three-dimensional (3D) structure of the major white matter fasciculi within living human brain. Specifically, we applied this technique to visualize in vivo (i) the superior longitudinal (arcuate) fasciculus, (ii) the inferior longitudinal fasciculus, (iii) the superior fronto-occipital (subcallosal) fasciculus, (iv) the inferior fronto-occipital fasciculus, (v) the uncinate fasciculus, (vi) the cingulum, (vii) the anterior commissure, (viii) the corpus callosum, (ix) the internal capsule, and (x) the fornix. These fasciculi were first isolated and were then interactively displayed as a 3D-rendered object. The virtual tract maps obtained in vivo using this approach were faithful to the classical descriptions of white matter anatomy that have previously been documented in postmortem studies. Since we have been able to interactively delineate and visualize white matter fasciculi over their entire length in vivo, in a manner that has only previously been possible by histological means, virtual in vivo interactive dissection (VIVID) adds a new dimension to anatomical descriptions of the living human brain

Evidence Of Distinctive Structural Alterations That Differentiate Adhd Boys With And Without A Comorbid Reading Disability

Attention deficit hyperactivity disorder (ADHD) and reading disability (RD) are neurodevelopmental disorders that often co-occur. Children with ADHD and co-occurring RD (ADHD/+RD) tend to show greater cognitive deficits than children with ADHD alone (ADHD/-RD). However, the extents to which comorbid RD impact structural alteration in children with ADHD have never been investigated. The overall goal of this study was to assess structural alterations in the subcortical, cortical and white matter that may differentiate ADHD/-RD from ADHD/+RD. The general hypothesis was that ADHD/+RD would show extensive alterations in regions implicated in ADHD than ADHD/-RD as well as show additional abnormalities in regions associated with RD. To this end, structural MRI and DTI scans obtained from 22 ADHD/-RD boys, 15 ADHD/+RD boys and 29 healthy control (HC) boys comparable in age and IQ were analyzed to assess alterations in striatal morphology, cortical thickness and white matter integrity. Analysis of the striatum showed greater and widespread alterations in the caudate in ADHD/+RD relative to ADHD/-RD but not putamen where the alterations were only seen in ADHD/-RD. Similarly, ADHD/+RD showed significantly thinner cortex in the regions associated with attention and cognitive control as well as additional regions associated with reading relative to ADHD/-RD and HC. Finally, analysis of DTI parameters showed greater extent of alteration in white matter architecture of the frontostriatal fiber tracts. Together, these findings provide evidence of excessive disturbances in the frontostriatal and frontoparietal networks that regulate executive functions, attention and cognitive control. Furthermore, there is evidence of additional alterations in the regions associated with reading skills. Overall, the results indicate a distinctive profile of structural alterations that differentiate ADHD/-RD from ADHD/+RD relative to HC and may underpin the greater neuropsychological impairments observed in ADHD/+RD

Assessing early white matter predictors of syntactic abilities in post-stroke aphasia using HARDI-based tractography

La recherche de prédicteurs d’habilités langagières en aphasie post-accident vasculaire cérébral (AVC) basés sur la matière blanche a récemment vu un élan. Cela a été motivé par l’émergence du modèle à double-voie où des faisceaux de matière blanche dorsaux et ventraux jouent un rôle important dans le langage, ainsi que par l’avènement de la tractographie basée sur l’imagerie par résonance magnétique (IRM) de diffusion permettant l’étude in-vivo des faisceaux de matière blanche et de leurs propriétés structurelles. Les caractéristiques structurelles et la charge lésionnelle des faisceaux de matière blanche ont permis de prédire les troubles langagiers dans la phase chronique dans quelques études. Cependant, les prédicteurs aigus de matière blanche des habilités syntaxiques en aphasie post-AVC chronique sont méconnus. L’exploitation de la tractographie dans l’étude des faisceaux langagiers de matière blanche a été limitée par plusieurs défis méthodologiques, dont la difficulté de reconstruire des faisceaux ayant une architecture complexe. Des progrès méthodologiques ont été récemment introduits afin d’adresser ces limites, dont le plus important est la tractographie basée sur l’imagerie à haute résolution angulaire (« HARDI »). Cependant, la fiabilité test-retest de la reconstruction et des propriétés structurelles d’une approche de tractographie HARDI de pointe n’a pas encore été évaluée. Le premier article de cette thèse visait à évaluer la fiabilité test-retest de la reconstruction et des propriétés structurelles (anisotropie fractionnelle, FA; diffusivité moyenne, axiale et radiale, MD, AD, RD; nombre d’orientations de fibres, NuFO; volume du faisceau; longueur moyenne des « streamlines ») de faisceaux langagiers majeurs (arqué, inférieur fronto-occipital, inférieur longitudinal, unciné, AF, IFOF, ILF, UF) obtenus avec une approche de tractographie HARDI de pointe. La majorité des mesures de propriétés structurelles ont montré une bonne ou excellente fiabilité. Ces résultats ont des implications importantes pour l’utilisation d’une telle approche pour l’étude des faisceaux langagiers de matière blanche, car ils renforcent la confiance dans la stabilité des reconstructions et les propriétés structurelles obtenus avec la tractographie HARDI. Le second article de cette thèse visait à déterminer si et quelles propriétés structurelles (FA, AD, volume du faisceau), et la charge lésionnelle, de l’AF et l’UF gauches dans la phase aigüe (≤ 3 jours), obtenus avec l’approche de tractographie HARDI utilisée dans le premier article, prédisent les habilités syntaxiques dans le discours spontané en aphasie post-AVC chronique (≥ 6 mois). Des régressions multiples ascendantes ont révélé que le volume de l’AF prédit la production des verbes, la complexité des phrases et la complexité de la structure argumentale du verbe. Le volume de l’UF a amélioré la prédiction de cette dernière. Ces résultats indiquent que le volume semble être un bon prédicteur précoce des habilités syntaxiques dans le discours spontané en aphasie post-AVC chronique. Mis ensemble, les résultats de cette thèse soulignent l’utilité d’une approche de tractographie HARDI de pointe et son potentiel pour le développement futur de biomarqueurs précoces pouvant améliorer le pronostic de patients ayant une aphasie post-AVC chronique. Cela pourrait promouvoir l’optimisation des soins et le développement de thérapies pour le bienfait des patients et leurs familles.The search for white matter predictors of language abilities in post-stroke aphasia has gained momentum in recent years. This growing interest has been driven by the emergence of the dual-stream framework where dorsal and ventral white matter bundles play an important functional role in language, as well as the advent of diffusion magnetic resonance imaging (MRI)-based tractography which allows the in-vivo investigation of white matter bundles and their structural properties. Structural characteristics, as well as the lesion load, of white matter bundles have been previously found to predict language impairments in the chronic phase. However, little is known about acute white matter predictors of syntactic abilities in chronic post-stroke aphasia. Leveraging tractography to study white matter language bundles has been limited by several methodological challenges, such as the difficulty of reconstructing white matter bundles with a complex fiber architecture. A number of methodological advances have been introduced fairly recently to address these limitations, the most important of which is the advent of tractography based on High Angular Resolution Imaging (HARDI). However, the test-retest reliability of the reconstruction and structural properties of a state-of-the-art HARDI-based tractography pipeline has not been previously assessed. The first article of the present thesis aimed to assess the test-retest reliability of the reconstruction and structural properties (fractional anisotropy, FA; mean, axial, radial diffusivity, MD, AD, RD; number of fiber orientations, NuFO; bundle volume; mean length of streamlines) of major white matter language bundles (arcuate, inferior fronto-occipital, inferior longitudinal, and uncinate fasciculi, AF, IFOF, ILF, UF) obtained using a state-of-the-art HARDI-based tractography pipeline. Most measures of structural properties showed good to excellent test-retest reliability. These findings have important implications for the use of such a pipeline for the study of white matter language bundles, as they increase our confidence that the reconstructions and structural properties obtained from the tractography pipeline are stable and not due to random variations in measurement. The second article of the thesis aimed to determine whether and which structural properties (FA, AD, bundle volume), as well as the lesion load, of the left AF and UF in the acute phase post-stroke (≤ 3 days), obtained with the same state-of-the-art HARDI-based tractography pipeline used in the first article, predict syntactic abilities in connected speech in chronic post-stroke aphasia (≥ 6 months). Forward multiple regressions revealed that the left AF’s volume predicted the percentage of verbs produced, the structural complexity of sentences, as well as verb-argument structure complexity. The left UF’s volume improved the prediction of verbs with a complex argument structure. These findings indicate that the bundle volume may be a good early predictor of syntactic ability in connected speech in chronic post-stroke aphasia. Overall, the findings of this thesis highlight the usefulness of a state-of-the-art HARDI-based tractography approach and its potential for the future development of early biomarkers that could improve the prognosis and personalized care of patients with chronic post-stroke aphasia. This would promote the optimization of patient care and the development of therapies for the benefit of patients and their families

A unified model of post-stroke language deficits including discourse production and their neural correlates.

The clinical profiles of individuals with post-stroke aphasia demonstrate considerable variation in the presentation of symptoms. Recent aphasiological studies have attempted to account for this individual variability using a multivariate data-driven approach (principal component analysis) on an extensive neuropsychological and aphasiological battery, to identify fundamental domains of post-stroke aphasia. These domains mainly reflect phonology, semantics and fluency; however, these studies did not account for variability in response to different forms of connected speech, i.e. discourse genres. In the current study, we initially examined differences in the quantity, diversity and informativeness between three different discourse genres, including a simple descriptive genre and two naturalistic forms of connected speech (storytelling narrative, and procedural discourse). Subsequently, we provided the first quantitative investigation on the multidimensionality of connected speech production at both behavioural and neural levels. Connected speech samples across descriptive, narrative, and procedural discourse genres were collected from 46 patients with chronic post-stroke aphasia and 20 neurotypical adults. Content analyses conducted on all connected speech samples indicated that performance differed across discourse genres and between groups. Specifically, storytelling narratives provided higher quantities of content words and lexical diversity compared to composite picture description and procedural discourse. The analyses further revealed that, relative to neurotypical adults, patients with aphasia, both fluent and non-fluent, showed reduction in the quantity of verbal production, lexical diversity, and informativeness across all discourses. Given the differences across the discourses, we submitted the connected speech metrics to principal component analysis alongside an extensive neuropsychological/aphasiological battery that assesses a wide range of language and cognitive skills. In contrast to previous research, three unique orthogonal connected speech components were extracted in a unified model, reflecting verbal quantity, verbal quality, and motor speech, alongside four core language and cognitive components: phonological production, semantic processing, phonological recognition, and executive functions. Voxel-wise lesion-symptom mapping using these components provided evidence on the involvement of widespread cortical regions and their white matter connections. Specifically, left frontal regions and their underlying white matter tracts corresponding to the frontal aslant tract and the anterior segment of the arcuate fasciculus were particularly engaged with the quantity and quality of fluent connected speech production while controlling for other co-factors. The neural correlates associated with the other language domains align with existing models on the ventral and dorsal pathways for language processing

fMRI-Targeted High-Angular Resolution Diffusion MR Tractography to Identify Functional Language Tracts in Healthy Controls and Glioma Patients

Background MR Tractography enables non-invasive preoperative depiction of language subcortical tracts, which is crucial for the presurgical work-up of brain tumors; however, it cannot evaluate the exact function of the fibers. Purpose A systematic pipeline was developed to combine tractography reconstruction of language fiber bundles, based on anatomical landmarks (Anatomical-T), with language fMRI cortical activations. A fMRI-targeted Tractography (fMRI-T) was thus obtained, depicting the subsets of the anatomical tracts whose endpoints are located inside a fMRI activation. We hypothesized that fMRI-T could provide additional functional information regarding the subcortical structures, better reflecting the eloquent white matter structures identified intraoperatively. Methods Both Anatomical-T and fMRI-T of language fiber tracts were performed on 16 controls and preoperatively on 16 patients with left-hemisphere brain tumors, using a q-ball residual bootstrap algorithm based on High Angular Resolution Diffusion Imaging (HARDI) datasets (b = 3000 s/mm(2); 60 directions); fMRI ROIs were obtained using picture naming, verbal fluency, and auditory verb generation tasks. In healthy controls, normalized MNI atlases of fMRI-T and Anatomical-T were obtained. In patients, the surgical resection of the tumor was pursued by identifying eloquent structures with intraoperative direct electrical stimulation mapping and extending surgery to the functional boundaries. Post-surgical MRI allowed to identify Anatomical-T and fMRI-T non-eloquent portions removed during the procedure. Results MNI Atlases showed that fMRI-T is a subset of Anatomical-T, and that different task-specific fMRI-T involve both shared subsets and task-specific subsets - e.g., verbal fluency fMRI-T strongly involves dorsal frontal tracts, consistently with the phonogical-articulatory features of this task. A quantitative analysis in patients revealed that Anatomical-T removed portions of AF-SLF and IFOF were significantly greater than verbal fluency fMRI-T ones, suggesting that fMRI-T is a more specific approach. In addition, qualitative analyses showed that fMRI-T AF-SLF and IFOF predict the exact functional limits of resection with increased specificity when compared to Anatomical-T counterparts, especially the superior frontal portion of IFOF, in a subcohort of patients. Conclusion These results suggest that performing fMRI-T in addition to the 'classic' Anatomical-T may be useful in a preoperative setting to identify the 'high-risk subsets' that should be spared during the surgical procedure

White matter microstructure disruption in early stage amyloid pathology.

Introduction: Amyloid beta (Aβ) accumulation is the first pathological hallmark of Alzheimer's disease (AD), and it is associated with altered white matter (WM) microstructure. We aimed to investigate this relationship at a regional level in a cognitively unimpaired cohort. Methods: We included 179 individuals from the European Medical Information Framework for AD (EMIF‐AD) preclinAD study, who underwent diffusion magnetic resonance (MR) to determine tract‐level fractional anisotropy (FA); mean, radial, and axial diffusivity (MD/RD/AxD); and dynamic [18F]flutemetamol) positron emission tomography (PET) imaging to assess amyloid burden. Results: Regression analyses showed a non‐linear relationship between regional amyloid burden and WM microstructure. Low amyloid burden was associated with increased FA and decreased MD/RD/AxD, followed by decreased FA and increased MD/RD/AxD upon higher amyloid burden. The strongest association was observed between amyloid burden in the precuneus and body of the corpus callosum (CC) FA and diffusivity (MD/RD) measures. In addition, amyloid burden in the anterior cingulate cortex strongly related to AxD and RD measures in the genu CC. Discussion: Early amyloid deposition is associated with changes in WM microstructure. The non‐linear relationship might reflect multiple stages of axonal damage

Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners.

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies

Impaired Structural Connectivity In Parkinson's Disease Patients With Mild Cognitive Impairment: A Study Based On Probabilistic Tractography

Background: Probabilistic tractography, in combination with graph theory, has been used to reconstruct the structural whole-brain connectome. Threshold-free network-based statistics (TFNBS) is a useful technique to study structural connectivity in neurodegenerative disorders; however, there are no previous studies using TFNBS in Parkinson's disease (PD) with and without mild cognitive impairment (MCI). Methods: Sixty-two PD patients, 27 of whom classified as PD-MCI, and 51 healthy controls (HC) underwent diffusion-weighted 3T MRI. Probabilistic tractography, using FSL, was used to compute the number of streamlines (NOS) between regions. NOS matrices were used to find group differences with TFNBS, and to calculate global and local measures of network integrity using graph theory. A binominal logistic regression was then used to assess the discrimination between PD with and without MCI using non-overlapping significant tracts. Tract-based spatial statistics (TBSS) were also performed with FSL to study changes in fractional anisotropy (FA) and mean diffusivity (MD). Results: PD-MCI showed 37 white matter (WM) connections with reduced connectivity strength compared to HC, mainly involving temporo-occipital regions. These were able to differentiate PD-MCI from PD without MCI with an area under the curve of 83-85%. PD without MCI showed disrupted connectivity in 18 connections involving fronto-temporal regions. No significant differences were found in graph measures. Only PD-MCI showed reduced FA compared with HC. Discussion: TFNBS based on whole-brain probabilistic tractography can detect structural connectivity alterations in PD with and without MCI. Reduced structural connectivity in fronto-striatal and posterior corticocortical connections is associated with PD-MCI

A CAD system for early diagnosis of autism using different imaging modalities.

The term “autism spectrum disorder” (ASD) refers to a collection of neuro-developmental disorders that affect linguistic, behavioral, and social skills. Autism has many symptoms, most prominently, social impairment and repetitive behaviors. It is crucial to diagnose autism at an early stage for better assessment and investigation of this complex syndrome. There have been a lot of efforts to diagnose ASD using different techniques, such as imaging modalities, genetic techniques, and behavior reports. Imaging modalities have been extensively exploited for ASD diagnosis, and one of the most successful ones is Magnetic resonance imaging(MRI),where it has shown promise for the early diagnosis of the ASD related abnormalities in particular. Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. After the advent in the nineteen eighties, MRI soon became one of the most promising non- invasive modalities for visualization and diagnostics of ASD-related abnormalities. Along with its main advantage of no exposure to radiation, high contrast, and spatial resolution, the recent advances to MRI modalities have notably increased diagnostic certainty. Multiple MRI modalities, such as different types of structural MRI (sMRI) that examines anatomical changes, and functional MRI (fMRI) that examines brain activity by monitoring blood flow changes,have been employed to investigate facets of ASD in order to better understand this complex syndrome. This work aims at developing a new computer-aided diagnostic (CAD) system for autism diagnosis using different imaging modalities. It mainly relies on making use of structural magnetic resonance images for extracting notable shape features from parts of the brainthat proved to correlate with ASD from previous neuropathological studies. Shape features from both the cerebral cortex (Cx) and cerebral white matter(CWM)are extracted. Fusion of features from these two structures is conducted based on the recent findings suggesting that Cx changes in autism are related to CWM abnormalities. Also, when fusing features from more than one structure, this would increase the robustness of the CAD system. Moreover, fMRI experiments are done and analyzed to find areas of activation in the brains of autistic and typically developing individuals that are related to a specific task. All sMRI findings are fused with those of fMRI to better understand ASD in terms of both anatomy and functionality,and thus better classify the two groups. This is one aspect of the novelty of this CAD system, where sMRI and fMRI studies are both applied on subjects from different ages to diagnose ASD. In order to build such a CAD system, three main blocks are required. First, 3D brain segmentation is applied using a novel hybrid model that combines shape, intensity, and spatial information. Second, shape features from both Cx and CWM are extracted and anf MRI reward experiment is conducted from which areas of activation that are related to the task of this experiment are identified. Those features were extracted from local areas of the brain to provide an accurate analysis of ASD and correlate it with certain anatomical areas. Third and last, fusion of all the extracted features is done using a deep-fusion classification network to perform classification and obtain the diagnosis report. Fusing features from all modalities achieved a classification accuracy of 94.7%, which emphasizes the significance of combining structures/modalities for ASD diagnosis. To conclude, this work could pave the pathway for better understanding of the autism spectrum by finding local areas that correlate to the disease. The idea of personalized medicine is emphasized in this work, where the proposed CAD system holds the promise to resolve autism endophenotypes and help clinicians deliver personalized treatment to individuals affected with this complex syndrome