scholarly journals Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions

2020 ◽  
Author(s):  
Joseph C. Griffis ◽  
Nicholas V. Metcalf ◽  
Maurizio Corbetta ◽  
Gordon L. Shulman
Keyword(s):  
White Matter ◽  
Grey Matter ◽  
Neurological Diseases ◽  
Brain Lesion ◽  
Software Tool ◽  
Brain Network ◽  
Brain Lesions ◽  
Matlab Software ◽  
Lesion Studies ◽  
Structural Impacts

AbstractLesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain’s structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions.

scholarly journals Brain disconnections link structural connectivity with function and behaviour

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Michel Thiebaut de Schotten ◽  
Chris Foulon ◽  
Parashkev Nachev
Keyword(s):  
White Matter ◽  
Brain Lesion ◽  
Structural Connectivity ◽  
Brain Lesions ◽  
Brain Areas ◽  
Brain Functions ◽  
The Past ◽  
Lesion Studies ◽  
Open Question

Abstract Brain lesions do not just disable but also disconnect brain areas, which once deprived of their input or output, can no longer subserve behaviour and cognition. The role of white matter connections has remained an open question for the past 250 years. Based on 1333 stroke lesions, here we reveal the human Disconnectome and demonstrate its relationship to the functional segregation of the human brain. Results indicate that functional territories are not only defined by white matter connections, but also by the highly stereotyped spatial distribution of brain disconnections. While the former has granted us the possibility to map 590 functions on the white matter of the whole brain, the latter compels a revision of the taxonomy of brain functions. Overall, our freely available Atlas of White Matter Function will enable improved clinical-neuroanatomical predictions for brain lesion studies and provide a platform for explorations in the domain of cognition.

scholarly journals Brain disconnections link structural connectivity with function and behaviour

2020 ◽  
Author(s):  
Michel Thiebaut de Schotten ◽  
Chris Foulon ◽  
Parashkev Nachev
Keyword(s):  
White Matter ◽  
Brain Lesion ◽  
Structural Connectivity ◽  
Brain Lesions ◽  
Brain Areas ◽  
Brain Functions ◽  
The Past ◽  
Lesion Studies ◽  
Open Question

AbstractBrain lesions do not just disable but also disconnect brain areas, which once deprived of their input or output, can no longer subserve behaviour and cognition. The role of white matter connections has remained an open question for the past 250 years. Based on 1333 stroke lesions we reveal the human Disconnectome and demonstrate its relationship to the functional segregation of the human brain. Results indicate that functional territories are not only defined by white matter connections, but also by the highly stereotyped spatial distribution of brain disconnections. While the former has granted us the possibility to map 590 functions on the white matter of the whole brain, the latter compels a revision of the taxonomy of brain functions. Overall, our freely available Functional Atlas of the White Matter will enable improved clinical-neuroanatomical predictions for brain lesion studies and provide a platform for novel explorations in the domain of cognition.

scholarly journals Synthesis of High-Resolution Research-Quality MRI Data from Clinical MRI Data in Patients with COVID-19

2021 ◽  
Author(s):  
Ryan J Cali ◽  
Holly J Freeman ◽  
Benjamin Billot ◽  
Megan E Barra ◽  
David Fischer ◽  
...  
Keyword(s):  
White Matter ◽  
High Resolution ◽  
Neurological Disorders ◽  
Grey Matter ◽  
Brain Lesion ◽  
Research Quality ◽  
Quality Data ◽  
Focal Lesions ◽  
Mri Scans ◽  
Clinical Mri

Pathophysiological mechanisms of neurological disorders in patients with coronavirus disease 2019 (COVID-19) are poorly understood, partly because of a lack of high-resolution neuroimaging data. We applied SynthSR, a convolutional neural network that synthesizes high-resolution isotropic research-quality data from thick-slice clinical MRI data, to a cohort of 11 patients with severe COVID-19. SynthSR successfully synthesized T1-weighted MPRAGE data at 1 mm spatial resolution for all 11 patients, each of whom had at least one brain lesion. Correlations between volumetric measures derived from synthesized and acquired MPRAGE data were strong for the cortical grey matter, subcortical grey matter, brainstem, hippocampus, and hemispheric white matter (r=0.84 to 0.96, p≤0.001), but absent for the cerebellar white matter and corpus callosum (r=0.04 to 0.17, p>0.61). SynthSR creates an opportunity to quantitatively study clinical MRI scans and elucidate the pathophysiology of neurological disorders in patients with COVID-19, including those with focal lesions.

scholarly journals Microstructural changes in the reward system are associated with post-stroke depression

2020 ◽  
Author(s):  
Lena KL Oestreich ◽  
Paul Wright ◽  
Michael J O’Sullivan
Keyword(s):  
Major Depressive Disorder ◽  
White Matter ◽  
Grey Matter ◽  
Reward System ◽  
Brain Network ◽  
Healthy Controls ◽  
Depression Severity ◽  
Major Depressive ◽  
Whole Brain ◽  
Post Stroke Depression

AbstractBackgroundStudies of lesion location have been unsuccessful in identifying simple mappings between single brain regions and post-stroke depression (PSD). This might partly reflect the involvement of multiple interconnected regions in the regulation of mood. In this study, we set out to investigate whole-brain network structure and white matter connectivity in the genesis of PSD. Based on studies implicating regions of the reward system in major depressive disorder without stroke, we investigated the overlap of whole-brain correlates of PSD with this system and performed a focused analysis of grey matter and white matter projections within the reward system and their associations with the development of PSD.MethodsThe study enrolled 46 patients with first ischemic stroke, 12 were found to have PSD (D+ group) and 34 were free of PSD (D-) based on scores on the Geriatric Depression Scale. A group of 16 healthy controls were also recruited. Participants underwent research MRI with 3T structural and diffusion sequences. Graph theoretical measures derived from measures of microstructure were used to examine global topology and whole-brain connectome analyses were employed to assess differences in the interregional connectivity matrix between the three groups. Structural correlates specific to the reward system were examined by measuring grey matter volumes from regions in this circuit and by reconstructing its main white matter pathways, namely the medial forebrain bundle and connections within the cingulum bundle with deterministic tractography. For network connections and tracts, we derived measures of microstructural organization (FA), and also extracellular free-water content (FW) as a possible proxy of neuroinflammation.ResultsThe topology of structural networks differed across the three groups. Network modularity, weighted by extracellular FW content, increased with depression severity and connectome analysis identified networks of decreased FA-weighted and increased FW-weighted connectivity in patients with PSD relative to healthy controls. Intrinsic frontal and fronto-subcortical connections were a notable feature of these networks, which also subsumed the majority of regions defined as constituting the reward system. Within the reward system, grey matter volume of cortical and subcortical regions, as well as FA and FW of major connection pathways, were collectively predictive of PSD severity, explaining 76.8% of the variance in depression severity.ConclusionsTaken together, these findings indicate that PSD is associated with microstructural characteristics of the reward system, similar to those observed in major depressive disorder without stroke. Alterations in the reward system appear to drive differences in whole-brain network structure found in patients with PSD. Even in the absence of a simple relationship with lesion size and location, neuroimaging measures can explain much of the variance in depression scores. Structural characterization of the reward system is a promising biomarker of vulnerability to depression after stroke.

Brain Lesions

2020 ◽  
Author(s):  
Ian Q. Whishaw ◽  
Megan Okuma
Keyword(s):  
18Th Century ◽  
Brain Lesion ◽  
Survival Rates ◽  
Brain Regions ◽  
Brain Lesions ◽  
Therapeutic Interventions ◽  
Diagnostic Tools ◽  
Compensatory Behaviors ◽  
Lesion Studies ◽  
The Brain

A brain lesion is an area of damage, injury, or abnormal change to a part of the brain. Brain lesions may be caused by head injury, disease, surgery, or congenital disorders, and they are classified by the cause, extent, and locus of injury. Lesions cause many behavioral symptoms. Symptom severity generally corresponds to the region and extent of damaged brain. Thus, behavior is often a reliable indicator of the type and extent of a lesion. Observations of patients suffering brain lesions were first recorded in detail in the 18th century, and lesion studies continue to shape modern neuroscience and to give insight into the functions of brain regions. Recovery, defined as any return of lost behavioral or cognitive function, depends on the age, sex, genetics, and lifestyle of patients, and recovery may be predicted by the cause of injury. Most recovery occurs within the first 6 to 9 months after injury and likely involves a combination of compensatory behaviors and physiological changes in the brain. Children often recover some function after brain lesions better than adults, though both children and adults experience residual deficits. Brain lesion survival rates are improved by better diagnostic tools and treatments. Therapeutic interventions and treatments for brain lesions include surgery, pharmaceuticals, transplants, and temperature regulation, each with varying degrees of success. Research in treating brain lesions is progressing, but in principle a cure will only be complete when brain lesions are replaced with healthy tissue.

scholarly journals White Matter Network Alterations in Alzheimer’s Disease Patients

2020 ◽  
Vol 10 (3) ◽  
pp. 919
Author(s):  
Ramesh Kumar Lama ◽  
Sang-Woong Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Grey Matter ◽  
Small World ◽  
Brain Network ◽  
Topological Properties ◽  
Graph Theoretical Analysis ◽  
Small World Property ◽  
The Brain

Previous studies have revealed the occurrence of alterations of white matter (WM) and grey matter (GM) microstructures in Alzheimer’s disease (AD) and their prodromal state amnestic mild cognitive impairment (MCI). In general, these alterations can be studied comprehensively by modeling the brain as a complex network, which describes many important topological properties, such as the small-world property, modularity, and efficiency. In this study, we systematically investigated white matter abnormalities using unbiased whole brain network analysis. We compared regional and network related WM features between groups of 19 AD and 25 MCI patients and 22 healthy controls (HC) using tract-based spatial statistics (TBSS), network based statistics (NBS) and graph theoretical analysis. We did not find significant differences in fractional anisotropy (FA) between two groups on TBSS analysis. However, observable alterations were noticed at a network level. Brain network measures such as global efficiency and small world properties were low in AD patients compared to HCs.

Individual differences in white and grey matter structure associated with verbal habits of thought

2019 ◽  
Author(s):  
Justin C. Hayes ◽  
Katherine L Alfred ◽  
Rachel Pizzie ◽  
Joshua S. Cetron ◽  
David J. M. Kraemer
Keyword(s):  
Individual Differences ◽  
White Matter ◽  
Cognitive Processing ◽  
Grey Matter ◽  
Structural Changes ◽  
Structural Integrity ◽  
Diffusion Tensor ◽  
Self Report ◽  
White Matter Tracts

Modality specific encoding habits account for a significant portion of individual differences reflected in functional activation during cognitive processing. Yet, little is known about how these habits of thought influence long-term structural changes in the brain. Traditionally, habits of thought have been assessed using self-report questionnaires such as the visualizer-verbalizer questionnaire. Here, rather than relying on subjective reports, we measured habits of thought using a novel behavioral task assessing attentional biases toward picture and word stimuli. Hypothesizing that verbal habits of thought are reflected in the structural integrity of white matter tracts and cortical regions of interest, we used diffusion tensor imaging and volumetric analyses to assess this prediction. Using a whole-brain approach, we show that word bias is associated with increased volume in several bilateral language regions, in both white and grey matter parcels. Additionally, connectivity within white matter tracts within an a priori speech production network increased as a function of word bias. These results demonstrate long-term structural and morphological differences associated with verbal habits of thought.

scholarly journals CSF proteome in multiple sclerosis subtypes related to brain lesion transcriptomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maria L. Elkjaer ◽  
Arkadiusz Nawrocki ◽  
Tim Kacprowski ◽  
Pernille Lassen ◽  
Anja Hviid Simonsen ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Protein Phosphatase ◽  
Brain Lesion ◽  
Brain Lesions ◽  
Cns Diseases ◽  
Secondary Progressive ◽  
Apolipoprotein A ◽  
Proteomic Approach ◽  
Apolipoprotein C ◽  
Altered Proteins

AbstractTo identify markers in the CSF of multiple sclerosis (MS) subtypes, we used a two-step proteomic approach: (i) Discovery proteomics compared 169 pooled CSF from MS subtypes and inflammatory/degenerative CNS diseases (NMO spectrum and Alzheimer disease) and healthy controls. (ii) Next, 299 proteins selected by comprehensive statistics were quantified in 170 individual CSF samples. (iii) Genes of the identified proteins were also screened among transcripts in 73 MS brain lesions compared to 25 control brains. F-test based feature selection resulted in 8 proteins differentiating the MS subtypes, and secondary progressive (SP)MS was the most different also from controls. Genes of 7 out these 8 proteins were present in MS brain lesions: GOLM was significantly differentially expressed in active, chronic active, inactive and remyelinating lesions, FRZB in active and chronic active lesions, and SELENBP1 in inactive lesions. Volcano maps of normalized proteins in the different disease groups also indicated the highest amount of altered proteins in SPMS. Apolipoprotein C-I, apolipoprotein A-II, augurin, receptor-type tyrosine-protein phosphatase gamma, and trypsin-1 were upregulated in the CSF of MS subtypes compared to controls. This CSF profile and associated brain lesion spectrum highlight non-inflammatory mechanisms in differentiating CNS diseases and MS subtypes and the uniqueness of SPMS.

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