Methods for the analysis and characterization of brain morphology from MRI images

Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. These measures can both allow a better understanding of how the brain changes due to multiple factors (e.g., environmental and pathological) and contribute to the identification of new imaging biomarkers of neurological and psychiatric diseases. The overall goal of the present thesis is to advance the knowledge on how brain MRI image processing can be effectively used to analyze and characterize brain structure. The first two works presented in this thesis are animal studies that primarily aim to use MRI data for analyzing differences between groups of interest. In Paper I, MRI scans from wild and domestic rabbits were processed to identify structural brain differences between these two groups. Domestication was found to significantly reshape brain structure in terms of both regional gray matter volume and white matter integrity. In Paper II, rat brain MRI scans were used to train a brain age prediction model. This model was then tested on both controls and a group of rats that underwent long-term environmental enrichment and dietary restriction. This healthy lifestyle intervention was shown to significantly affect the predicted brain age trajectories by slowing the rats’ aging process compared to controls. Furthermore, brain age predicted on young adult rats was found to have a significant effect on survival. Papers III to V are human studies that propose deep learning-based methods for segmenting brain structures that can be severely affected by neurodegeneration. In particular, Papers III and IV focus on U-Net-based 2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS) patients. In both studies, good segmentation accuracy was obtained and a significant correlation was found between CC area and the patient’s level of cognitive and physical disability. Additionally, in Paper IV, shape analysis of the segmented CC revealed a significant association between disability and both CC thickness and bending angle. Conversely, in Paper V, a novel method for automatic segmentation of the hippocampus is proposed, which consists of embedding a statistical shape prior as context information into a U-Net-based framework. The inclusion of shape information was shown to significantly improve segmentation accuracy when testing the method on a new unseen cohort (i.e., different from the one used for training). Furthermore, good performance was observed across three different diagnostic groups (healthy controls, subjects with mild cognitive impairment and Alzheimer’s patients) that were characterized by different levels of hippocampal atrophy. In summary, the studies presented in this thesis support the great value of MRI image analysis for the advancement of neuroscientific knowledge, and their contribution is mostly two-fold. First, by applying well-established processing methods on datasets that had not yet been explored in the literature, it was possible to characterize specific brain changes and disentangle relevant problems of a clinical or biological nature. Second, a technical contribution is provided by modifying and extending already-existing brain image processing methods to achieve good performance on new datasets

The humanised CYP2C19 transgenic mouse exhibits cerebellar atrophy and movement impairment reminiscent of ataxia

Aims: CYP2C19 transgenic mouse expresses the human CYP2C19 gene in the liver and developing brain, and it exhibits altered neurodevelopment associated with impairments in emotionality and locomotion. Because the validation of new animal models is essential for the understanding of the aetiology and pathophysiology of movement disorders, the objective was to characterise motoric phenotype in CYP2C19 transgenic mice and to investigate its validity as a new animal model of ataxia. Methods: The rotarod, paw-print and beam-walking tests were utilised to characterise the motoric phenotype. The volumes of 20 brain regions in CYP2C19 transgenic and wild-type mice were quantified by 9.4T gadolinium-enhanced post-mortem structural neuroimaging. Antioxidative enzymatic activity was quantified biochemically. Dopaminergic alterations were characterised by chromatographic quantification of concentrations of dopamine and its metabolites and by subsequent immunohistochemical analyses. The beam-walking test was repeated after the treatment with dopamine receptor antagonists ecopipam and raclopride. Results: CYP2C19 transgenic mice exhibit abnormal, unilateral ataxia-like gait, clasping reflex and 5.6-fold more paw-slips in the beam-walking test; the motoric phenotype was more pronounced in youth. Transgenic mice exhibited a profound reduction of 12% in cerebellar volume and a moderate reduction of 4% in hippocampal volume; both regions exhibited an increased antioxidative enzyme activity. CYP2C19 mice were hyperdopaminergic; however, the motoric impairment was not ameliorated by dopamine receptor antagonists, and there was no alteration in the number of midbrain dopaminergic neurons in CYP2C19 mice. Conclusions: Humanised CYP2C19 transgenic mice exhibit altered gait and functional motoric impairments; this phenotype is likely caused by an aberrant cerebellar development

Methods for the analysis and characterization of brain morphology from MRI images

Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. These measures can both allow a better understanding of how the brain changes due to multiple factors (e.g., environmental and pathological) and contribute to the identification of new imaging biomarkers of neurological and psychiatric diseases. The overall goal of the present thesis is to advance the knowledge on how brain MRI image processing can be effectively used to analyze and characterize brain structure. The first two works presented in this thesis are animal studies that primarily aim to use MRI data for analyzing differences between groups of interest. In Paper I, MRI scans from wild and domestic rabbits were processed to identify structural brain differences between these two groups. Domestication was found to significantly reshape brain structure in terms of both regional gray matter volume and white matter integrity. In Paper II, rat brain MRI scans were used to train a brain age prediction model. This model was then tested on both controls and a group of rats that underwent long-term environmental enrichment and dietary restriction. This healthy lifestyle intervention was shown to significantly affect the predicted brain age trajectories by slowing the rats' aging process compared to controls. Furthermore, brain age predicted on young adult rats was found to have a significant effect on survival. Papers III to V are human studies that propose deep learning-based methods for segmenting brain structures that can be severely affected by neurodegeneration. In particular, Papers III and IV focus on U-Net-based 2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS) patients. In both studies, good segmentation accuracy was obtained and a significant correlation was found between CC area and the patient's level of cognitive and physical disability. Additionally, in Paper IV, shape analysis of the segmented CC revealed a significant association between disability and both CC thickness and bending angle. Conversely, in Paper V, a novel method for automatic segmentation of the hippocampus is proposed, which consists of embedding a statistical shape prior as context information into a U-Net-based framework. The inclusion of shape information was shown to significantly improve segmentation accuracy when testing the method on a new unseen cohort (i.e., different from the one used for training). Furthermore, good performance was observed across three different diagnostic groups (healthy controls, subjects with mild cognitive impairment and Alzheimer's patients) that were characterized by different levels of hippocampal atrophy. In summary, the studies presented in this thesis support the great value of MRI image analysis for the advancement of neuroscientific knowledge, and their contribution is mostly two-fold. First, by applying well-established processing methods on datasets that had not yet been explored in the literature, it was possible to characterize specific brain changes and disentangle relevant problems of a clinical or biological nature. Second, a technical contribution is provided by modifying and extending already-existing brain image processing methods to achieve good performance on new datasets.Magnetresonansbilder (MR-bilder) används för att framställa detaljerade bilder av hjärnan utan joniserande strålning. Från en strukturell MR-bild är det möjligt att extrahera morfologiska egenskaper hos hjärnans olika regioner, såsom deras volym och form. Dessa egenskaper kan ge bättre förståelse för förändringar som hjärnan utsätts för på grund av en mängd faktorer (exempelvis miljö eller sjukdom) samt bidra till att identifiera nya bildbaserade biomarkörer för neurologiska och psykiatriska sjukdomar. Den här avhandlingens huvudsakliga mål är att bidra till kunskapen om hur bildbehandling av MR-bilder kan användas för att analysera och karaktärisera hjärnstrukturer. De två första delarbetena som ingår i avhandlingen är djurstudier som primärt avser att använda MR-data för att analysera skillnaderna mellan två kohorter. I Artikel I behandlas MR-bilder från domesticerade och vilda kaniner för att identifiera skillnader i hjärnstruktur mellan de två grupperna. Domesticering visade sig förändra hjärnstrukturen signifikant, både den gråa hjärnsubstansens volym och den vita hjärnsubstansens integritet. I Artikel II användes MR-bilder på råttor för att träna en datadriven modell att predicera hjärnålder. Modellen testades sedan på en kontrollgrupp och en grupp råttor som under flera månader utsattes för en mer stimulerande miljö samt fick en diet med restriktioner. Den mer hälsosamma livsstilen visade sig bidra till en lägre predicerad hjärnålder genom att sakta ner råttornas åldringsprocess, jämfört med kontrollgruppen. Hjärnåldern hos unga, vuxna råttor visade sig signifikant påverka råttornas överlevnad. Artikel III, IV och V är människostudier som föreslår djupinlärningsbaserade metoder för att segmentera (avgränsa) hjärnstrukturer som kan påverkas av neurodegeneration. Artikel III och IV i synnerhet fokuserar på U-Net-baserad 2D-segmentering av corpus callosum (CC) hos patienter med multipel skleros. I båda studierna uppmättes god träffsäkerhet för segmenteringsalgoritmen och signifikant korrelation mellan CC:s area och patientens kognitiva och fysiska nedsättning. Utöver detta visar Artikel IV genom geometrisk analys av den segmenterade CC ett signifikant samband mellan sjukdom och CC:s tjocklek och böjvinkel. I Artikel V introduceras en ny metod för automatisk segmentering av hippocampus. Metoden kombinerar U-Net-baserad segmentering med en inbyggd statistisk representation av hippocampus’ form. Metoden visade sig ge en signifikant förbättring av segmenteringskvaliteten när metoden utvärderades på en ny, tidigare osedd, kohort. Goda resultat uppmättes även i tre olika diagnosgrupper (en frisk kontrollgrupp, patienter med milda kognitiva symptom och en grupp patienter med Alzheimers sjukdom) som särskilde sig genom tre olika nivåer av atrofi av hippocampus. Sammanfattningsvis bidrar studierna som ingår i avhandlingen till att förstärka värdet av MR-bildanalys för framsteg inom neurovetenskapen, och detta på två sätt. Genom att applicera väletablerade bildbehandlingsmetoder på dataset som ännu inte utforskats i litteraturen var det möjligt att karaktärisera specifika förändringar i hjärnans geometri och därmed lösa relevanta kliniska eller biologiska utmaningar. Vidare har studierna  bidragit till den teknologiska metodutvecklingen genom att modifiera och utvidga existerande bildbehandlingsmetoder för hjärnbilder för att uppnå goda resultat på nya dataset.QC 2022-02-28</p

Methods for the analysis and characterization of brain morphology from MRI images

Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. These measures can both allow a better understanding of how the brain changes due to multiple factors (e.g., environmental and pathological) and contribute to the identification of new imaging biomarkers of neurological and psychiatric diseases. The overall goal of the present thesis is to advance the knowledge on how brain MRI image processing can be effectively used to analyze and characterize brain structure. The first two works presented in this thesis are animal studies that primarily aim to use MRI data for analyzing differences between groups of interest. In Paper I, MRI scans from wild and domestic rabbits were processed to identify structural brain differences between these two groups. Domestication was found to significantly reshape brain structure in terms of both regional gray matter volume and white matter integrity. In Paper II, rat brain MRI scans were used to train a brain age prediction model. This model was then tested on both controls and a group of rats that underwent long-term environmental enrichment and dietary restriction. This healthy lifestyle intervention was shown to significantly affect the predicted brain age trajectories by slowing the rats' aging process compared to controls. Furthermore, brain age predicted on young adult rats was found to have a significant effect on survival. Papers III to V are human studies that propose deep learning-based methods for segmenting brain structures that can be severely affected by neurodegeneration. In particular, Papers III and IV focus on U-Net-based 2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS) patients. In both studies, good segmentation accuracy was obtained and a significant correlation was found between CC area and the patient's level of cognitive and physical disability. Additionally, in Paper IV, shape analysis of the segmented CC revealed a significant association between disability and both CC thickness and bending angle. Conversely, in Paper V, a novel method for automatic segmentation of the hippocampus is proposed, which consists of embedding a statistical shape prior as context information into a U-Net-based framework. The inclusion of shape information was shown to significantly improve segmentation accuracy when testing the method on a new unseen cohort (i.e., different from the one used for training). Furthermore, good performance was observed across three different diagnostic groups (healthy controls, subjects with mild cognitive impairment and Alzheimer's patients) that were characterized by different levels of hippocampal atrophy. In summary, the studies presented in this thesis support the great value of MRI image analysis for the advancement of neuroscientific knowledge, and their contribution is mostly two-fold. First, by applying well-established processing methods on datasets that had not yet been explored in the literature, it was possible to characterize specific brain changes and disentangle relevant problems of a clinical or biological nature. Second, a technical contribution is provided by modifying and extending already-existing brain image processing methods to achieve good performance on new datasets.Magnetresonansbilder (MR-bilder) används för att framställa detaljerade bilder av hjärnan utan joniserande strålning. Från en strukturell MR-bild är det möjligt att extrahera morfologiska egenskaper hos hjärnans olika regioner, såsom deras volym och form. Dessa egenskaper kan ge bättre förståelse för förändringar som hjärnan utsätts för på grund av en mängd faktorer (exempelvis miljö eller sjukdom) samt bidra till att identifiera nya bildbaserade biomarkörer för neurologiska och psykiatriska sjukdomar. Den här avhandlingens huvudsakliga mål är att bidra till kunskapen om hur bildbehandling av MR-bilder kan användas för att analysera och karaktärisera hjärnstrukturer. De två första delarbetena som ingår i avhandlingen är djurstudier som primärt avser att använda MR-data för att analysera skillnaderna mellan två kohorter. I Artikel I behandlas MR-bilder från domesticerade och vilda kaniner för att identifiera skillnader i hjärnstruktur mellan de två grupperna. Domesticering visade sig förändra hjärnstrukturen signifikant, både den gråa hjärnsubstansens volym och den vita hjärnsubstansens integritet. I Artikel II användes MR-bilder på råttor för att träna en datadriven modell att predicera hjärnålder. Modellen testades sedan på en kontrollgrupp och en grupp råttor som under flera månader utsattes för en mer stimulerande miljö samt fick en diet med restriktioner. Den mer hälsosamma livsstilen visade sig bidra till en lägre predicerad hjärnålder genom att sakta ner råttornas åldringsprocess, jämfört med kontrollgruppen. Hjärnåldern hos unga, vuxna råttor visade sig signifikant påverka råttornas överlevnad. Artikel III, IV och V är människostudier som föreslår djupinlärningsbaserade metoder för att segmentera (avgränsa) hjärnstrukturer som kan påverkas av neurodegeneration. Artikel III och IV i synnerhet fokuserar på U-Net-baserad 2D-segmentering av corpus callosum (CC) hos patienter med multipel skleros. I båda studierna uppmättes god träffsäkerhet för segmenteringsalgoritmen och signifikant korrelation mellan CC:s area och patientens kognitiva och fysiska nedsättning. Utöver detta visar Artikel IV genom geometrisk analys av den segmenterade CC ett signifikant samband mellan sjukdom och CC:s tjocklek och böjvinkel. I Artikel V introduceras en ny metod för automatisk segmentering av hippocampus. Metoden kombinerar U-Net-baserad segmentering med en inbyggd statistisk representation av hippocampus’ form. Metoden visade sig ge en signifikant förbättring av segmenteringskvaliteten när metoden utvärderades på en ny, tidigare osedd, kohort. Goda resultat uppmättes även i tre olika diagnosgrupper (en frisk kontrollgrupp, patienter med milda kognitiva symptom och en grupp patienter med Alzheimers sjukdom) som särskilde sig genom tre olika nivåer av atrofi av hippocampus. Sammanfattningsvis bidrar studierna som ingår i avhandlingen till att förstärka värdet av MR-bildanalys för framsteg inom neurovetenskapen, och detta på två sätt. Genom att applicera väletablerade bildbehandlingsmetoder på dataset som ännu inte utforskats i litteraturen var det möjligt att karaktärisera specifika förändringar i hjärnans geometri och därmed lösa relevanta kliniska eller biologiska utmaningar. Vidare har studierna  bidragit till den teknologiska metodutvecklingen genom att modifiera och utvidga existerande bildbehandlingsmetoder för hjärnbilder för att uppnå goda resultat på nya dataset.QC 2022-02-28</p

metadata

Metadata of training dataset

Age-Specific Adult Rat Brain MRI Templates and Tissue Probability Maps

Age-specific resources in human MRI mitigate processing biases that arise from structural changes across the lifespan. There are fewer age-specific resources for preclinical imaging, and they only represent developmental periods rather than adulthood. Since rats recapitulate many facets of human aging, it was hypothesized that brain volume and each tissue's relative contribution to total brain volume would change with age in the adult rat. Data from a longitudinal study of rats at 3, 5, 11, and 17 months old were used to test this hypothesis. Tissue volume was estimated from high resolution structural images using a priori information from tissue probability maps. However, existing tissue probability maps generated inaccurate gray matter probabilities in subcortical structures, particularly the thalamus. To address this issue, gray matter, white matter, and CSF tissue probability maps were generated by combining anatomical and signal intensity information. The effects of age on volumetric estimations were then assessed with mixed-effects models. Results showed that herein estimation of gray matter volumes better matched histological evidence, as compared to existing resources. All tissue volumes increased with age, and the tissue proportions relative to total brain volume varied across adulthood. Consequently, a set of rat brain templates and tissue probability maps from across the adult lifespan is released to expand the preclinical MRI community's fundamental resources