Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network

Purpose: The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. / Methods: Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A “traveling brains” dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. / Results: The results show an inter-center and inter-subject variability of <10%, except for “clustering coefficient” (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners’ hardware. / Conclusions: The results show low variability of connectivity topological metrics across sites running a harmonised protocol

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments

Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p &lt; .001; accuracy: 70%, p &lt; .001) and the CV-model (AUC: .59, p &lt; .001; accuracy: 64%, p &lt; .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

New neurophysiological and imaging methods for detection of microstructural changes in mild traumatic brain injury

Mild traumatic brain injury is a very common health problem. Although outcome is generally good, a significant proportion of patients have persistent symptoms or an incomplete functional recovery. The mechanisms of this are incompletely understood, but believed to include microstructural injuries that may be undetectable by presently used diagnostic tests. This thesis aims at exploring new diagnostic methods that could be utilised in examining mild traumatic brain injury. I study tested transcranial magnetic stimulation defined motor thresholds in a sample of chronic phase mild traumatic brain injury patients. Elevated motor thresholds were found compared to healthy controls, associated with altered excitability of the corticospinal tract. II study used transcranial magnetic stimulation combined with electroencephalography to probe responses of frontal brain regions. The employed method is reported to be sensitive to changes in excitability and connectivity of the brain. Differences were found between samples of fully recovered and persistently symptomatic patients with mild traumatic brain injury and healthy controls. On basis of this, transcranial magnetic stimulation and electroencephalography could be used to detect functional changes that are not paralleled by lesions on routine magnetic resonance imaging. III study compared diffusion tensor imaging based deterministic tractography and a newer method, based on constrained spherical deconvolution, automatic, deep learning based segmentation and probabilistic tractography. Participants were patients with symptomatic mild traumatic brain injury and healthy controls. The newer approach was able to find differences between the groups, while diffusion tensor method was not. This suggests the new approach may be more sensitive in detecting microstructural changes related to mild traumatic brain injury. These results show that mild traumatic brain injury can be associated with functional and structural changes in the absence of trauma-related findings on routine MRI. The methods evaluated may provide new ways to detect these changes.Uusia neurofysiologisia ja kuvantamismenetelmiä lievään aivovammaan liittyvien mikrorakenteellisten muutosten toteamisessa Lievä aivovamma on erittäin tavallinen. Toipuminen on yleensä hyvää, mutta osalle potilaista jää pitkäkestoisia oireita tai toimintakyvyn vajavuutta. Näiden syntymekanismia ei täysin ymmärretä, mutta ajatellaan sen voivan liittyä aivojen mikrorakenteellisiin muutoksiin, joiden toteamiseen nykyiset diagnostiset testit voivat olla riittämättömiä. Tämä väitöstutkimus selvittää uusia keinoja, joita voitaisiin hyödyntää lievän aivovamman arvioinnissa. I osatyössä tutkittiin transkraniaalisen magneettistimulaation avulla motorisia kynnyksiä. Tutkimusjoukkona oli lievän aivovamman saaneita, kroonisen vaiheen potilaita. Potilasjoukolla todettiin terveisiin verrokkeihin nähden korkeampia motorisia kynnyksiä, joka liittyy muutoksiin kortikospinaaliradan ärtyvyydessä. II osatyö hyödynsi transkraniaalista magneettistimulaatiota ja elektroenkefalografiaa frontaalisten aivoalueiden vasteiden tutkimisessa. Aiempien julkaisujen perusteella menetelmä on herkkä aivojen ärtyvyyden ja aivoalueiden välisten yhteyksien muutosten toteamisessa. Menetelmällä löydettiin eroja lievästä aivovammasta oireettomiksi toipuneista, pitkäkestoisesti oireilevista ja terveistä verrokeista koostuneiden osallistujajoukkojen välillä. Transkraniaalisen magneettistimulaation ja elektroenkefalografian yhdistelmällä saatetaan siten havaita toimin-nallisia muutoksia, joille ei ole vastinetta tavallisissa magneettikuvissa. III osatyössä verrattiin diffuusiotensorikuvantamista ja determinististä traktografiaa uudempaan menetelmään, joka perustui constrained spherical deconvolution -laskentaan, automaattiseen, syväoppimiseen perustuvaan segmentaatioon ja probabilistiseen traktografiaan. Tutkimusjoukkona oli lievän aivovamman saaneita, oireisia potilaita ja terveitä verrokkeja. Uudella menetelmällä löydettiin eroja ryhmien välillä, mutta vertailumenetelmällä eroja ei havaittu. Tällä perusteella uusi menetelmä vaikuttaa herkemmältä aivovammaan liittyvien mikrorakenteellisten muutosten toteamisessa. Tulokset osoittavat, että lievään aivovammaan voi liittyä toiminnallisia ja rakenteellisia muutoksia, vaikka tavanomaisen magneettikuvauksen löydös olisi normaali. Näiden muutosten toteaminen voi olla mahdollista arvioiduilla menetelmillä

Lead-DBS v3.0: Mapping Deep Brain Stimulation Effects to Local Anatomy and Global Networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics

The Role of Long-Range Connectivity for the Characterization of the Functional–Anatomical Organization of the Cortex

This review focuses on the role of long-range connectivity as one element of brain structure that is of key importance for the functional–anatomical organization of the cortex. In this context, we discuss the putative guiding principles for mapping brain function and structure onto the cortical surface. Such mappings reveal a high degree of functional–anatomical segregation. Given that brain regions frequently maintain characteristic connectivity profiles and the functional repertoire of a cortical area is closely related to its anatomical connections, long-range connectivity may be used to define segregated cortical areas. This methodology is called connectivity-based parcellation. Within this framework, we investigate different techniques to estimate connectivity profiles with emphasis given to non-invasive methods based on diffusion magnetic resonance imaging (dMRI) and diffusion tractography. Cortical parcellation is then defined based on similarity between diffusion tractograms, and different clustering approaches are discussed. We conclude that the use of non-invasively acquired connectivity estimates to characterize the functional–anatomical organization of the brain is a valid, relevant, and necessary endeavor. Current and future developments in dMRI technology, tractography algorithms, and models of the similarity structure hold great potential for a substantial improvement and enrichment of the results of the technique

Diffusion and Perfusion MRI in Paediatric Posterior Fossa Tumours

Brain tumours in children frequently occur in the posterior fossa. Most undergo surgical resection, after which up to 25% develop cerebellar mutism syndrome (CMS), characterised by mutism, emotional lability and cerebellar motor signs; these typically improve over several months. This thesis examines the application of diffusion (dMRI) and arterial spin labelling (ASL) perfusion MRI in children with posterior fossa tumours. dMRI enables non-invasive in vivo investigation of brain microstructure and connectivity by a computational process known as tractography. The results of a unique survey of British neurosurgeons’ attitudes towards tractography are presented, demonstrating its widespread adoption and numerous limitations. State-of-the-art modelling of dMRI data combined with tractography is used to probe the anatomy of cerebellofrontal tracts in healthy children, revealing the first evidence of a topographic organization of projections to the frontal cortex at the superior cerebellar peduncle. Retrospective review of a large institutional series shows that CMS remains the most common complication of posterior fossa tumour resection, and that surgical approach does not influence surgical morbidity in this cohort. A prospective case-control study of children with posterior fossa tumours treated at Great Ormond Street Hospital is reported, in which children underwent longitudinal MR imaging at three timepoints. A region-of-interest based approach did not reveal any differences in dMRI metrics with respect to CMS status. However, the candidate also conducted an analysis of a separate retrospective cohort of medulloblastoma patients at Stanford University using an automated tractography pipeline. This demonstrated, in unprecedented spatiotemporal detail, a fine-grained evolution of changes in cerebellar white matter tracts in children with CMS. ASL studies in the prospective cohort showed that following tumour resection, increases in cortical cerebral blood flow were seen alongside reductions in blood arrival time, and these effects were modulated by clinical features of hydrocephalus and CMS. The results contained in this thesis are discussed in the context of the current understanding of CMS, and the novel anatomical insights presented provide a foundation for future research into the condition

Development of Advanced, Clinically Feasible Neuroimaging Methodology with Diffusional Kurtosis Imaging

Diffusion MRI (dMRI) is a powerful, non-invasive tool for probing the structural organization of the human brain. Quantitative dMRI analyses provide unique capabilities for the characterization of tissue microstructure as well as imaging contrast that is not available to other modalities. White matter tractography relies on dMRI and is currently the only non-invasive technique for mapping structural connections in the human brain. In this chapter, we will describe diffusional kurtosis imaging, an effective and versatile dMRI technique, and discuss a clinical problem in temporal lobe epilepsy (TLE) which is insurmountable with current diagnostic approaches. Subsequent chapters will further develop the capabilities of DKI and demonstrate how it may be particularly well suited to overcome current barriers to care in the clinical management of TLE