Cortical Terminations of the Inferior Fronto-Occipital and Uncinate Fasciculi: Anatomical Stem-Based Virtual Dissection

International audienceWe combined the neuroanatomists' approach of defining a fascicle as all fibers passing through its compact stem with diffusion-weighted tractography to investigate the cortical terminations of two association tracts, the inferior fronto-occipital fasciculus (IFOF) and the uncinate fasciculus (UF), which have recently been implicated in the ventral language circuitry. The aim was to provide a detailed and quantitative description of their terminations in 60 healthy subjects and to do so to apply an anatomical stem-based virtual dissection, mimicking classical post-mortem dissection, to extract with minimal a priori the IFOF and UF from tractography datasets. In both tracts, we consistently observed more extensive termination territories than their conventional definitions, within the middle and superior frontal, superior parietal and angular gyri for the IFOF and the middle frontal gyrus and superior, middle and inferior temporal gyri beyond the temporal pole for the UF. We revealed new insights regarding the internal organization of these tracts by investigating for the first time the frequency, distribution and hemispheric asymmetry of their terminations. Interestingly, we observed a dissociation between the lateral right-lateralized and medial left-lateralized fronto-occipital branches of the IFOF. In the UF, we observed a rightward lateralization of the orbito-frontal and temporal branches. We revealed a more detailed map of the terminations of these fiber pathways that will enable greater specificity for correlating with diseased populations and other behavioral measures. The limitations of the diffusion tensor model in this study are also discussed. We conclude that anatomical stem-based virtual dissection with diffusion tractography is a fruitful method for studying the structural anatomy of the human white matter pathways

White matter fibres dissection in the human brain

PhD ThesisIntroduction: lesion to white matter fibres can induce permanent neurological deficits due to the induction of disconnection syndromes. Knowledge of white matter fibre anatomy is therefore relevant to the neurosurgeon in order to minimise the risk of causing neurological damage when approaching lesions in eloquent areas of the brain. Aim: to investigate the 3D anatomy of white matter fibres with particular attention to the associative tracts, including short arcuate fibres and intralobar fibres. The results obtained will be used to provide insights in brain connectivity, delineating networks important for specific brain functions. Methods: The Klingler technique for white matter dissection was followed. Brain specimens were collected and prepared at the Newcastle Brain Tissue Resource, Newcastle University. Brains were initially fixed in 10% formalin for at least 4 weeks. After removing the pia-mater and arachnoid, the brains were frozen at -15C° for 2 weeks. The water crystallisation induced by the freezing process separates the white matter fibres, facilitating the dissection of the tracts. Dissection was performed with wooden spatulas and blunt metallic dissectors, removing the cortex and exposing the white matter. The short associative (U-shaped) fibres were initially exposed. Long associative, commissural and projection fibres were demonstrated as the dissection proceeded. Results: five papers form the main body of the present work: 1) “Raymond de Vieussens and his contribution to the study of white matter anatomy”. This historical paper reviewed the history of white matter dissection, focusing on the work of Raymond de Vieussens, who gave the first account of the centrum ovale and of the continuity of the corticospinal tract from the centrum ovale to the brainstem. 2) “The white matter of the human cerebrum: part I The occipital lobe by Heinrich Sachs “ ; 3) “Intralobar fibres of the occipital lobe: A post mortem dissection study”. These joint papers were dedicated to the white matter anatomy of the occipital lobe. A rich network of association fibres, arranged around the ventricular wall, was demonstrated. A new white matter tract, connecting the cuneus to the lingula, was also described. Our original data I II were compared to the atlas of occipital fibres produced by the German anatomist Heinrich Sachs. 4) “White matter connections of the Supplementary Motor Area (SMA) in humans”. This study demonstrated that the SMA shows a wide range of connections with motor, language and limbic areas. Features of the SMA syndrome (akinesia and mutism) can be better understood on the basis of these findings. 5) “Anatomical connections of the Subgenual Cingulate Region” (SCG). This study showed that the SCG is at the centre of a large network, connecting prefrontal, limbic and mesotemporal regions. The connectivity of this region can help explain the clinical effect of neuromodulaton of the SCG in Deep Brain Stimulation for neuropsychiatric disorders. Conclusions: Klingler dissection provided original data about the connections of different brain regions that are relevant to neurosurgical practice, along with the description of a new white matter tract, connecting the cuneus to the lingula

Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury

pre-printAvailable approaches to the investigation of traumatic brain injury (TBI) are frequently hampered, to some extent, by the unsatisfactory abilities of existing methodologies to efficiently define and represent affected structural connectivity and functional mechanisms underlying TI-related pathology. In this paper, we describe a patient-tailored framework which allows mapping and characterization of TBI-related structural damage to the brain via multimodal neuroimaging and personalized connectomics. Specifically, we introduce a graphically driven approach for the assessment of trauma-related atrophy of white matter connections between cortical structures, with relevance to the quantification of TBI chronic case evolution. This approach allows one to inform the formulation of graphical neurophysiology and neurophysiology TBI profiles based on the particular structural deficits of the affected patient. In addition, it allows one to relate the findings supplied by our workflow to the existing body of research that focuses on the functional roles of the cortical structures being targeted. A graphical means for representing patient TBI status is relevant to the emerging field of personalized medicine and to the investigation of neural atrophy

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process

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

HUMAN BRAIN WHITE MATTER ANALYSIS USING TRACTOGRAPHY —AN ATLAS-BASED APPROACH

The human brain is connected via a vastly complex network of white matter fiber pathways. However, this structural connectivity information cannot be obtained from conventional MRI, in which much of white matter appears homogeneous. Diffusion tensor imaging can estimate fiber orientation by measuring the anisotropy of water diffusion. Using tractography, the brain connectivity can be studied non-invasively. Past tractography studies have shown that the cores of prominent white matter tracts can be faithfully reconstructed. Superimposing the tract coordinates on various MR images, MR metrics can be quantified in a tract-specific manner. However, tractography results are often contaminated by partial volume effect and imaging noise. Particularly, tractography often fails under white matter pathological conditions, which render tract-specific analysis impractical. In order to address these issues, we introduced an atlas-based approach. Four novel atlas-based approaches were included in this data analysis framework. First, statistical templates of major white matter tracts were created using a DTI database of normal subjects. The statistical white matter tract templates can serve two purposes. First, the statistical template can be used as a reference to detect abnormal white matter anatomy in neurodegenerative diseases. Second, the statistical template can be applied to individual patient data for automated white matter parcellation and tract-specific quantification. In the second approach, the trajectory of white matter fiber bundles was used to estimate the cortical regions associated with specific tracts of interest. Using this approach, cortical regions were reproducibly identified on the population-averaged cortical maps of brain connectivity. Third, we improved the accuracy of the population-based tract analysis by incorporating a highly elastic image transformation technique, called Large Deformation Diffeomorphic Metric Mapping (LDDMM). As a testament to the power of this algorithm, we successfully applied tract-specific analysis on Alzheimer’s patients. The last approach was to analyze the brain cortical connection networks using automatic fiber tracking. A tracking pipeline was built by combining White Matter Parcellation Map (WMPM), brute-force tractography and topology-preserving image transformation LDDMM. This novel tracking pipeline was applied on patient group with Alzheimer’s disease. The connectivity networks of Alzheimer’s patients were compared with age-matched controls using multivariate pattern classification

Patient-Tailored Connectomics Visualization for the Assessment of White Matter Atrophy in Traumatic Brain Injury

Available approaches to the investigation of traumatic brain injury (TBI) are frequently hampered, to some extent, by the unsatisfactory abilities of existing methodologies to efficiently define and represent affected structural connectivity and functional mechanisms underlying TBI-related pathology. In this paper, we describe a patient-tailored framework which allows mapping and characterization of TBI-related structural damage to the brain via multimodal neuroimaging and personalized connectomics. Specifically, we introduce a graphically driven approach for the assessment of trauma-related atrophy of white matter connections between cortical structures, with relevance to the quantification of TBI chronic case evolution. This approach allows one to inform the formulation of graphical neurophysiological and neuropsychological TBI profiles based on the particular structural deficits of the affected patient. In addition, it allows one to relate the findings supplied by our workflow to the existing body of research that focuses on the functional roles of the cortical structures being targeted. A graphical means for representing patient TBI status is relevant to the emerging field of personalized medicine and to the investigation of neural atrophy

Development and application of a human cortical brain atlas on MRI considering phylogeny = Développement et emploi d’un atlas du cortex cérébral humain réalisé sur IRM et tenant compte de la phylogénie

Le cortex cérébral est une structure en couches complexe qui remplit différents types de fonctions. Au cours de l’histoire des neurosciences, plusieurs atlas corticaux ont été développés pour classifier différentes régions du cortex en tant que zones aux caractéristiques structurelles ou fonctionnelles communes, afin d'étudier et de quantifier les changements aux états sain et pathologique. Cependant, il n'existe pas d'atlas suivant une approche phylogénétique, c'est-à-dire, basée sur les critères d'évolution communs. Ce mémoire présente les étapes de création d'un nouvel atlas dans un modèle d’imagerie par résonance magnétique (IRM) en espace standard (pseudo-Talairach) : le PAN-Atlas, basé sur l'origine phylogénétique commune de chaque zone corticale, et son application sur des scans d’IRM de dix individus pour évaluer sa performance. D’abord, nous avons regroupé les différentes régions corticales en cinq régions d'intérêt (RdI) d'origine phylogénétique connue (archicortex, paléocortex, périarchicortex, proïsocortex, isocortex ou néocortex) sur la base de protocoles de segmentation validés histologiquement par d'autres groupes de chercheurs. Puis, nous avons segmenté ces régions manuellement sur le modèle d’IRM cérébrale moyen MNI-ICBM 2009c, en formant des masques. Par la suite, on a utilisé un pipeline multi-étapes de traitement des images pour réaliser le recalage des masques de notre atlas aux scans pondérés T1 de dix participants sains, en obtenant ainsi des masques automatiques pour chaque RdI. Les masques automatiques ont été évalués après une correction manuelle par le biais de l’indice Dice-kappa, qui quantifie la colocalisation des voxels de chaque masque automatique vs. le masque corrigé manuellement. L’indice a montré une très bonne à excellente performance de notre atlas. Cela a permis l’évaluation et comparaison des volumes corticales de chaque région et la quantification des valeurs de transfert de magnétisation (ITM), qui sont sensibles à la quantité de myéline présente dans le tissu. Ce travail montre que la division régionale du cortex en IRM avec une approche phylogénétique est réalisable à l'aide de notre PAN-Atlas en espace standard et que les masques peuvent être utilisés pour différents types de quantifications, comme les volumes corticaux, ou l’estimation des valeurs de ITM. Notre atlas pourrait éventuellement servir à évaluer les différences entre personnes saines et celles atteintes par des maladies neurodégénératives ou d’autres maladies neurologiques.The cerebral cortex is a complex layered structure that performs different types of functions. Throughout the history of neuroscience, several cortical atlases have been developed to classify/divide different regions of the cortex into areas with common structural or functional characteristics, to then study and quantify changes in healthy and pathological states. However, to date, there is no atlas following a phylogenetic approach, i.e. based on the common evolution criteria. This thesis presents the steps of creation of a new atlas corresponding to a standard MRI template: the PAN-Atlas, based on the common phylogenetic origin of each cortical zone, and its application on MRI scans of ten healthy participants to assess its performance. First, we grouped the different cortical regions into five regions of interest (ROI) of known phylogenetic origin (archicortex, paleocortex, periarchicortex, proisocortex, isocortex or neocortex) based on MRI protocols previously validated through histology by other groups of researchers. Then, we manually segmented these ROIs on the MNI-ICBM 2009c average brain MRI template, creating corresponding masks. We then used a multi-step image processing pipeline to register the atlas’ masks to T1 weighted images of ten healthy participants, generating automatic masks for each scan. The accuracy of these automatic atlas’ masks was assessed after manual correction using Dice-kappa similarity index, to quantify the colocalization of the automatic vs. the manually corrected masks. The Dice-kappa values showed a very good to excellent performance of the automatic atlas’ masks. This allowed the evaluation and comparison of cortical volumes of each ROI, as well as the quantification of magnetization transfer ratio (MTR) values, which are sensitive to myelin content. This work shows that the division of the cortex on MRI following a phylogenetic approach is feasible using our PAN Atlas, and that the masks of the atlas can be used to perform different types of quantifications, such as the ones presented here (cortical volume and MTR per ROI). Our atlas could similarly be used to assess differences between the cortex of healthy individuals and people affected by neurodegenerative diseases and other neurological disorders

Identifying Changes of Functional Brain Networks using Graph Theory

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgement