Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC).

BACKGROUND: Predictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality. METHODS: RPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models. RESULTS: The distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06-2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56-2.62; P = 0.00647). CONCLUSIONS: RPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.

Test-retest reliability and longitudinal analysis of automated hippocampal subregion volumes in healthy ageing and Alzheimer's disease populations.

The hippocampal formation is a complex brain structure that is important in cognitive processes such as memory, mood, reward processing and other executive functions. Histological and neuroimaging studies have implicated the hippocampal region in neuropsychiatric disorders as well as in neurodegenerative diseases. This highly plastic limbic region is made up of several subregions that are believed to have different functional roles. Therefore, there is a growing interest in imaging the subregions of the hippocampal formation rather than modelling the hippocampus as a homogenous structure, driving the development of new automated analysis tools. Consequently, there is a pressing need to understand the stability of the measures derived from these new techniques. In this study, an automated hippocampal subregion segmentation pipeline, released as a developmental version of Freesurfer (v6.0), was applied to T1-weighted magnetic resonance imaging (MRI) scans of 22 healthy older participants, scanned on 3 separate occasions and a separate longitudinal dataset of 40 Alzheimer's disease (AD) patients. Test-retest reliability of hippocampal subregion volumes was assessed using the intra-class correlation coefficient (ICC), percentage volume difference and percentage volume overlap (Dice). Sensitivity of the regional estimates to longitudinal change was estimated using linear mixed effects (LME) modelling. The results show that out of the 24 hippocampal subregions, 20 had ICC scores of 0.9 or higher in both samples; these regions include the molecular layer, granule cell layer of the dentate gyrus, CA1, CA3 and the subiculum (ICC > 0.9), whilst the hippocampal fissure and fimbria had lower ICC scores (0.73-0.88). Furthermore, LME analysis of the independent AD dataset demonstrated sensitivity to group and individual differences in the rate of volume change over time in several hippocampal subregions (CA1, molecular layer, CA3, hippocampal tail, fissure and presubiculum). These results indicate that this automated segmentation method provides a robust method with which to measure hippocampal subregions, and may be useful in tracking disease progression and measuring the effects of pharmacological intervention.

Evolution of structural abnormalities in the rat brain following in utero exposure to maternal immune activation: A longitudinal in vivo MRI study.

Genetic and environmental risk factors for psychiatric disorders are suggested to disrupt the trajectory of brain maturation during adolescence, leading to the development of psychopathology in adulthood. Rodent models are powerful tools to dissect the specific effects of such risk factors on brain maturational profiles, particularly when combined with Magnetic Resonance Imaging (MRI; clinically comparable technology). We therefore investigated the effect of maternal immune activation (MIA), an epidemiological risk factor for adult-onset psychiatric disorders, on rat brain maturation using atlas and tensor-based morphometry analysis of longitudinal in vivo MR images. Exposure to MIA resulted in decreases in the volume of several cortical regions, the hippocampus, amygdala, striatum, nucleus accumbens and unexpectedly, the lateral ventricles, relative to controls. In contrast, the volumes of the thalamus, ventral mesencephalon, brain stem and major white matter tracts were larger, relative to controls. These volumetric changes were maximal between post-natal day 50 and 100 with no differences between the groups thereafter. These data are consistent with and extend prior studies of brain structure in MIA-exposed rodents. Apart from the ventricular findings, these data have robust face validity to clinical imaging findings reported in studies of individuals at high clinical risk for a psychiatric disorder. Further work is now required to address the relationship of these MRI changes to behavioral dysfunction and to establish thier cellular correlates.

Bringing memory fMRI to the clinic: comparison of seven memory fMRI protocols in temporal lobe epilepsy.

fMRI is increasingly implemented in the clinic to assess memory function. There are multiple approaches to memory fMRI, but limited data on advantages and reliability of different methods. Here, we compared effect size, activation lateralisation, and between-sessions reliability of seven memory fMRI protocols: Hometown Walking (block design), Scene encoding (block design and event-related design), Picture encoding (block and event-related), and Word encoding (block and event-related). All protocols were performed on three occasions in 16 patients with temporal lobe epilepsy (TLE). Group T-maps showed activity bilaterally in medial temporal lobe for all protocols. Using ANOVA, there was an interaction between hemisphere and seizure-onset lateralisation (P = 0.009) and between hemisphere, protocol and seizure-onset lateralisation (P = 0.002), showing that the distribution of memory-related activity between left and right temporal lobes differed between protocols and between patients with left-onset and right-onset seizures. Using voxelwise intraclass Correlation Coefficient, between-sessions reliability was best for Hometown and Scenes (block and event). The between-sessions spatial overlap of activated voxels was also greatest for Hometown and Scenes. Lateralisation of activity between hemispheres was most reliable for Scenes (block and event) and Words (event). Using receiver operating characteristic analysis to explore the ability of each fMRI protocol to classify patients as left-onset or right-onset TLE, only the Words (event) protocol achieved a significantly above-chance classification of patients at all three sessions. We conclude that Words (event) protocol shows the best combination of between-sessions reliability of the distribution of activity between hemispheres and reliable ability to distinguish between left-onset and right-onset patients.

Implantation site and lesion topology determine efficacy of a human neural stem cell line in a rat model of chronic stroke.

Stroke remains one of the most promising targets for cell therapy. Thorough preclinical efficacy testing of human neural stem cell (hNSC) lines in a rat model of stroke (transient middle cerebral artery occlusion) is, however, required for translation into a clinical setting. Magnetic resonance imaging (MRI) here confirmed stroke damage and allowed the targeted injection of 450,000 hNSCs (CTX0E03) into peri-infarct tissue, rather than the lesion cyst. Intraparenchymal cell implants improved sensorimotor dysfunctions (bilateral asymmetry test) and motor deficits (footfault test and rotameter). Importantly, analyses based on lesion topology (striatal vs. striatal + cortical damage) revealed a more significant improvement in animals with a stroke confined to the striatum. However, no improvement in learning and memory (water maze) was evident. An intracerebroventricular injection of cells did not result in any improvement. MRI-based lesion, striatal and cortical volumes were unchanged in treated animals compared to those with stroke that received an intraparenchymal injection of suspension vehicle. Grafted cells only survived after intraparenchymal injection with a striatal + cortical topology resulting in better graft survival (16,026 cells) than in animals with smaller striatal lesions (2,374 cells). Almost 20% of cells differentiated into glial fibrillary acidic protein+ astrocytes, but <2% turned into FOX3+ neurons. These results indicate that CTX0E03 implants robustly recover behavioral dysfunction over a 3-month time frame and that this effect is specific to their site of implantation. Lesion topology is potentially an important factor in the recovery, with a stroke confined to the striatum showing a better outcome compared to a larger area of damage.

White matter integrity assessed by diffusion tensor tractography in a patient with a large tumor mass but minimal clinical and neuropsychological deficits.

Diffusion tensor imaging (DTI) tractography and image registration were used to investigate a patient with a massive left-sided brain tumor, whose size was largely disproportionate to his subtle neurological deficits. MRI was obtained from the patient and his healthy identical twin, who acted as anatomical reference for DTI and as a control for quantitative measures. To compensate for the patient's altered anatomy, seed and way points for probabilistic tractography were drawn on the color-coded direction maps of the healthy twin. Registration, based on the combination of b0-images, T2-weighted and T1-weighted images, was used to identify the corresponding regions in the patient. The corticospinal tract (CST), the superior longitudinal fasciculus (SLF), and the cingulum bundle (CB) showed displaced anatomy. A significant difference was found between fractional anisotropy distribution along the left SLF and CB, but not along the CST. These findings fit well with the patient's substantial preservation of his motor abilities, while abnormalities of the SLF and CB could explain the subtle but detectable cognitive deficits.

A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease.

Background: Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. Methods: We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). Results: The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. Conclusions: This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.

Alzheimer's disease is the most common cause of dementia, impacting memory, thinking and behaviour. It can be challenging to diagnose Alzheimer's disease which can lead to suboptimal patient care. During the development of Alzheimer's disease the brain shrinks and the cells within it die. One method that can be used to assess brain function is magnetic resonance imaging, which uses magnetic fields and radio waves to produce images of the brain. In this study, we develop a method that uses magnetic resonance imaging data to identify differences in the brain between people with and without Alzheimer's disease, including before obvious shrinkage of the brain occurs. This method could be used to help diagnose patients with Alzheimer's Disease.

Segmentation of the thalamus in MRI based on T1 and T2.

Reliable identification of thalamic nuclei is required to improve positioning of electrodes in Deep Brain Stimulation (DBS), and to allow the role of individual thalamic nuclei in health and disease to be fully investigated. In this work, a previously proposed method for identifying sub-regions within the thalamus based on differences in their T1 and T2 values is explored in detail. The effect on the segmentation of T1 and T2 dependence weighted against priors for spatial position and extent was investigated. When T1 and T2 dependence was highly weighted, good distinction between identified regions was obtained in T1/T2 feature-space, but no contiguous anatomically distinct regions were identified within the thalamus. Incorporating spatial priors was necessary to ensure anatomically distinct regions were defined. Optimal values for segmentation parameters were obtained by assessing performance on a 'synthetic thalamus'. Using these optimum input parameters, within- and between-subjects reproducibility was assessed. Good reproducibility was obtained when six regions were specified to be identified in the thalamus. The six regions identified were similar in the majority of the normal subject group. However, intriguingly these regions were different from those obtained in the same subjects using a well-known connectivity-based segmentation technique. This method shows promise to identify intrathalamic structures on the basis of T1 and T2 signal. A comprehensive characterisation of thalamic nuclei may require a fully multi-modal approach.

A comprehensive testing protocol for MRI neuroanatomical segmentation techniques: Evaluation of a novel lateral ventricle segmentation method.

Although a wide range of approaches have been developed to automatically assess the volume of brain regions from MRI, the reproducibility of these algorithms across different scanners and pulse sequences, their accuracy in different clinical populations and sensitivity to real changes in brain volume have not always been comprehensively examined. Firstly we present a comprehensive testing protocol which comprises 312 freely available MR images to assess the accuracy, reproducibility and sensitivity of automated brain segmentation techniques. Accuracy is assessed in infants, young adults and patients with Alzheimer's disease in comparison to gold standard measures by expert observers using a manual technique based on Cavalieri's principle. The protocol determines the reliability of segmentation between scanning sessions, different MRI pulse sequences and 1.5T and 3T field strengths and examines their sensitivity to small changes in volume using a large longitudinal dataset. Secondly we apply this testing protocol to a novel algorithm for segmenting the lateral ventricles and compare its performance to the widely used FSL FIRST and FreeSurfer methods. The testing protocol produced quantitative measures of accuracy, reliability and sensitivity of lateral ventricle volume estimates for each segmentation method. The novel algorithm showed high accuracy in all populations (intraclass correlation coefficient, ICC>0.95), good reproducibility between MRI pulse sequences (ICC>0.99) and was sensitive to age related changes in longitudinal data. FreeSurfer demonstrated high accuracy (ICC>0.95), good reproducibility (ICC>0.99) and sensitivity whilst FSL FIRST showed good accuracy in young adults and infants (ICC>0.90) and good reproducibility (ICC=0.98), but was unable to segment ventricular volume in patients with Alzheimer's disease or healthy subjects with large ventricles. Using the same computer system, the novel algorithm and FSL FIRST processed a single MRI image in less than 10min while FreeSurfer took approximately 7h. The testing protocol presented enables the accuracy, reproducibility and sensitivity of different algorithms to be compared. We also demonstrate that the novel segmentation algorithm and FreeSurfer are both effective in determining lateral ventricular volume and are well suited for multicentre and longitudinal MRI studies.

Reproducibility of thalamic segmentation based on probabilistic tractography.

Reliable identification of thalamic nuclei is required to improve targeting of electrodes used in Deep Brain Stimulation (DBS), and for exploring the role of thalamus in health and disease. A previously described method using probabilistic tractography to segment the thalamus based on connections to cortical target regions was implemented. Both within- and between-subject reproducibility were quantitatively assessed by the overlap of the resulting segmentations; the effect of two different numbers of target regions (6 and 31) on reproducibility of the segmentation results was also investigated. Very high reproducibility was observed when a single dataset was processed multiple times using different starting conditions. Thalamic segmentation was also very reproducible when multiple datasets from the same subject were processed using six cortical target regions. Within-subject reproducibility was reduced when the number of target regions was increased, particularly in medial and posterior regions of the thalamus. A large degree of overlap in segmentation results from different subjects was obtained, particularly in thalamic regions classified as connecting to frontal, parietal, temporal and pre-central cortical target regions.

Neuroanatomical and Microglial Alterations in the Striatum of Levodopa-Treated, Dyskinetic Hemi-Parkinsonian Rats.

Dyskinesia associated with chronic levodopa treatment in Parkinson's disease is associated with maladaptive striatal plasticity. The objective of this study was to examine whether macroscale structural changes, as captured by magnetic resonance imaging (MRI) accompany this plasticity and to identify plausible cellular contributors in a rodent model of levodopa-induced dyskinesia. Adult male Sprague-Dawley rats were rendered hemi-parkinsonian by stereotaxic injection of 6-hydroxydopamine into the left medial forebrain bundle prior to chronic treatment with saline (control) or levodopa to induce abnormal involuntary movements (AIMs), reflective of dyskinesia. Perfusion-fixed brains underwent ex vivo structural MRI before sectioning and staining for cellular markers. Chronic treatment with levodopa induced significant AIMs (p < 0.0001 versus saline). The absolute volume of the ipsilateral, lesioned striatum was increased in levodopa-treated rats resulting in a significant difference in percentage volume change when compared to saline-treated rats (p < 0.01). Moreover, a significant positive correlation was found between this volume change and AIMs scores for individual levodopa-treated rats (r = 0.96; p < 0.01). The density of Iba1+ cells was increased within the lesioned versus intact striatum (p < 0.01) with no difference between treatment groups. Conversely, Iba1+ microglia soma size was significantly increased (p < 0.01) in the lesioned striatum of levodopa-treated but not saline-treated rats. Soma size was not, however, significantly correlated with either AIMs or MRI volume change. Although GFAP+ astrocytes were elevated in the lesioned versus intact striatum (p < 0.001), there was no difference between treatment groups. No statistically significant effects of either lesion or treatment on RECA1, a marker for blood vessels, were observed. Collectively, these data suggest chronic levodopa treatment in 6-hydroxydopamine lesioned rats is associated with increased striatal volume that correlates with the development of AIMs. The accompanying increase in number and size of microglia, however, cannot alone explain this volume expansion. Further multi-modal studies are warranted to establish the brain-wide effects of chronic levodopa treatment.

Monitoring Response to Transarterial Chemoembolization in Hepatocellular Carcinoma Using 18F-Fluorothymidine PET.

Accurate disease monitoring is essential after transarterial chemoembolization (TACE) in hepatocellular carcinoma (HCC) because of the potential for profound adverse events and large variations in survival outcome. Posttreatment changes on conventional imaging can confound determination of residual or recurrent disease, magnifying the clinical challenge. On the basis of increased expression of thymidylate synthase (TYMS), thymidine kinase 1 (TK-1), and equilibrative nucleoside transporter 1 (SLC29A1) in HCC compared with liver tissue, we conducted a proof-of-concept study evaluating the efficacy of 3'-deoxy-3'-18F-fluorothymidine (18F-FLT) PET to assess response to TACE. Because previous PET studies in HCC have been hampered by high background liver signal, we investigated whether a temporal-intensity voxel clustering (kinetic spatial filtering, or KSF) improved lesion detection. Methods: A tissue microarray was built from 36 HCC samples and from matching surrounding cirrhotic tissue and was stained for TK-1 A prospective study was conducted; 18 patients with a diagnosis of HCC by the criteria of the American Association for the Study of Liver Diseases who were eligible for treatment with TACE were enrolled. The patients underwent baseline conventional imaging and dynamic 18F-FLT PET with KSF followed by TACE. Imaging was repeated 6-8 wk after TACE. The PET parameters were compared with modified enhancement-based RECIST. Results: Cancer Genome Atlas analysis revealed increased RNA expression of TYMS, TK-1, and SLC29A1 in HCC. TK-1 protein expression was significantly higher in HCC (P < 0.05). The sensitivity of 18F-FLT PET for baseline HCC detection was 73% (SUVmax, 9.7 ± 3.0; tumor to liver ratio, 1.2 ± 0.3). Application of KSF did not improve lesion detection. Lesion response after TACE by modified RECIST was 58% (14 patients with 24 lesions). A 30% reduction in mean 18F-FLT PET uptake was observed after TACE, correlating with an observed PET response of 60% (15/25). A significant and profound reduction in the radiotracer delivery parameter K1 after TACE was observed. Conclusion:18F-FLT PET can differentiate HCC from surrounding cirrhotic tissue, with PET parameters correlating with TACE response. KSF did not improve visualization of tumor lesions. These findings warrant further investigation.

Accuracy assessment of global and local atrophy measurement techniques with realistic simulated longitudinal Alzheimer's disease images.

The evaluation of atrophy quantification methods based on magnetic resonance imaging have been usually hindered by the lack of realistic gold standard data against which to judge these methods or to help refine them. Recently [Camara, O., Schweiger, M., Scahill, R., Crum, W., Sneller, B., Schnabel, J., Ridgway, G., Cash, D., Hill, D., Fox, N., 2006. Phenomenological model of diffuse global and regional atrophy using finite-element methods. IEEE Trans. Med.l Imaging 25, 1417-1430], we presented a technique in which atrophy is realistically simulated in different tissue compartments or neuroanatomical structures with a phenomenological model. In this study, we have generated a cohort of realistic simulated Alzheimer's disease (AD) images with known amounts of atrophy, mimicking a set of 19 real controls and 27 probable AD subjects, with an improved version of our atrophy simulation methodology. This database was then used to assess the accuracy of several well-known computational anatomy methods which provide global (BSI and SIENA) or local (Jacobian integration) estimates of longitudinal atrophy in brain structures using MR images. SIENA and BSI results correlated very well with gold standard data (Pearson coefficient of 0.962 and 0.969 respectively), achieving small mean absolute differences with respect to the gold standard (percentage change from baseline volume): BSI of 0.23%+/-0.26%; SIENA of 0.22%+/-0.28%. Jacobian integration was guided by both fluid and FFD-based registration techniques and resulting deformation fields and associated Jacobians were compared, region by region, with gold standard ones. The FFD-based technique outperformed the fluid one in all evaluated structures (mean absolute differences from the gold standard in percentage change from baseline volume): whole brain, FFD=0.31%, fluid=0.58%; lateral ventricles, FFD=0.79%; fluid=1.45%; left hippocampus, FFD=0.82%; fluid=1.42%; right hippocampus, FFD=0.95%; fluid=1.62%. The largest errors for both local techniques occurred in the sulcal CSF (FFD=2.27%; fluid=3.55%) regions. For large structures such as the whole brain, these mean absolute differences, relative to the applied atrophy, represented similar percentages for the BSI, SIENA and FFD techniques (controls/patients): BSI, 51.99%/16.36%; SIENA, 62.34%/21.59%; FFD, 41.02%/24.95%. For small structures such as the hippocampi, these percentages were larger, especially for controls where errors were approximately equal to the small applied changes (controls/patients): FFD, 92.82%/43.61%. However, these apparently large relative errors have not prevented the global or hippocampal measures from finding significant group separation in our study. The evaluation framework presented here will help in quantifying whether the accuracy of future methodological developments is sufficient for analysing change in smaller or less atrophied local brain regions. Results obtained in our experiments with realistic simulated data confirm previously published estimates of accuracy for both evaluated global techniques. Regarding Jacobian Integration methods, the FFD-based one demonstrated promising results and potential for being used in clinical studies alongside (or in place of) the more common global methods. The generated gold standard data has also allowed us to identify some stages and sets of parameters in the evaluated techniques--the brain extraction step in the global techniques and the number of multi-resolution levels and the stopping criteria in the registration-based methods--that are critical for their accuracy.

Correlations between apolipoprotein E epsilon4 gene dose and whole brain atrophy rates.

OBJECTIVE: The purpose of this study was to characterize the relationship between whole brain atrophy rates and three levels of genetic risk for Alzheimer's disease in cognitively normal persons. The authors previously found accelerated whole brain atrophy rates in patients with probable Alzheimer's disease by computing changes in brain volume from sequential magnetic resonance images (MRIs). METHODS: The authors assessed 36 late-middle-aged persons from three genetic groups: those with two, one, and no copies of the apolipoprotein E (APOE) epsilon4 allele, a common Alzheimer's disease susceptibility gene. The participants had clinical ratings, neuropsychological tests, and volumetric T1-weighted MRIs during a baseline visit and again approximately 2 years later. Two different image-analysis techniques, brain boundary shift integration and iterative principal component analysis, were used to compute whole brain atrophy rates. RESULTS: While there were no baseline, follow-up, or between-visit differences in the clinical ratings or neuropsychological test scores among the three subject groups, whole brain atrophy rates were significantly greater in the epsilon4 homozygote group than in noncarriers and were significantly correlated with epsilon4 gene dose (i.e., the number of epsilon4 alleles in a person's APOE genotype). CONCLUSION: Since APOE epsilon4 gene dose is associated with an increased risk of Alzheimer's disease and a younger median age at dementia onset, this study suggests an association between the risk of Alzheimer's disease and accelerated brain atrophy rates before the onset of cognitive impairment.

A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression.

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous nonrigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.

Diamagnetic chemical exchange saturation transfer (diaCEST) affords magnetic resonance imaging of extracellular matrix hydrogel implantation in a rat model of stroke.

Extracellular matrix (ECM) is widely used as an inductive biological scaffold to repair soft tissue after injury by promoting functional site-appropriate remodeling of the implanted material. However, there is a lack of non-invasive analysis methods to monitor the remodeling characteristics of the ECM material after implantation and its biodegradation over time. We describe the use of diamagnetic chemical exchange saturation transfer (CEST) magnetic resonance imaging to monitor the distribution of an ECM hydrogel after intracerebral implantation into a stroke cavity. In vitro imaging indicated a robust concentration-dependent detection of the ECM precursor and hydrogel at 1.8 and 3.6 ppm, which broadly corresponded to chondroitin sulfate and fibronectin. This detection was robust to changes in pH and improved at 37 °C. In vivo implantation of ECM hydrogel into the stroke cavity in a rat model corresponded macroscopically to the distribution of biomaterial as indicated by histology, but mismatches were also evident. Indeed, CEST imaging detected an endogenous "increased deposition". To account for this endogenous activity, pre-implantation images were subtracted from post-implantation images to yield a selective visualization of hydrogel distribution in the stroke cavity and its evolution over 7 days. The CEST detection of ECM returned to baseline within 3 days due to a decrease in fibronectin and chondroitin sulfate in the hydrogel. The distribution of ECM hydrogel within the stroke cavity is hence feasible in vivo, but further advances are required to warrant a selective long-term monitoring in the context of biodegradation.

Correlations of Behavioral Deficits with Brain Pathology Assessed through Longitudinal MRI and Histopathology in the HdhQ150/Q150 Mouse Model of Huntington's Disease.

A variety of mouse models have been developed that express mutant huntingtin (mHTT) leading to aggregates and inclusions that model the molecular pathology observed in Huntington's disease. Here we show that although homozygous HdhQ150 knock-in mice developed motor impairments (rotarod, locomotor activity, grip strength) by 36 weeks of age, cognitive dysfunction (swimming T maze, fear conditioning, odor discrimination, social interaction) was not evident by 94 weeks. Concomitant to behavioral assessments, T2-weighted MRI volume measurements indicated a slower striatal growth with a significant difference between wild type (WT) and HdhQ150 mice being present even at 15 weeks. Indeed, MRI indicated significant volumetric changes prior to the emergence of the "clinical horizon" of motor impairments at 36 weeks of age. A striatal decrease of 27% was observed over 94 weeks with cortex (12%) and hippocampus (21%) also indicating significant atrophy. A hypothesis-free analysis using tensor-based morphometry highlighted further regions undergoing atrophy by contrasting brain growth and regional neurodegeneration. Histology revealed the widespread presence of mHTT aggregates and cellular inclusions. However, there was little evidence of correlations between these outcome measures, potentially indicating that other factors are important in the causal cascade linking the molecular pathology to the emergence of behavioral impairments. In conclusion, the HdhQ150 mouse model replicates many aspects of the human condition, including an extended pre-manifest period prior to the emergence of motor impairments.

Magnetic resonance imaging and tensor-based morphometry in the MPTP non-human primate model of Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder producing a variety of motor and cognitive deficits with the causes remaining largely unknown. The gradual loss of the nigrostriatal pathway is currently considered the pivotal pathological event. To better understand the progression of PD and improve treatment management, defining the disease on a structural basis and expanding brain analysis to extra-nigral structures is indispensable. The anatomical complexity and the presence of neuromelanin, make the use of non-human primates an essential element in developing putative imaging biomarkers of PD. To this end, ex vivo T2-weighted magnetic resonance images were acquired from control and 1-methyl-4 phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated marmosets. Volume measurements of the caudate, putamen, and substantia nigra indicated significant atrophy and cortical thinning. Tensor-based morphometry provided a more extensive and hypothesis free assessment of widespread changes caused by the toxin insult to the brain, especially highlighting regional cortical atrophy. The results highlight the importance of developing imaging biomarkers of PD in non-human primate models considering their distinct neuroanatomy. It is essential to further develop these biomarkers in vivo to provide non-invasive tools to detect pre-symptomatic PD and to monitor potential disease altering therapeutics.

Generalized overlap measures for evaluation and validation in medical image analysis.

Measures of overlap of labelled regions of images, such as the Dice and Tanimoto coefficients, have been extensively used to evaluate image registration and segmentation algorithms. Modern studies can include multiple labels defined on multiple images yet most evaluation schemes report one overlap per labelled region, simply averaged over multiple images. In this paper, common overlap measures are generalized to measure the total overlap of ensembles of labels defined on multiple test images and account for fractional labels using fuzzy set theory. This framework allows a single "figure-of-merit" to be reported which summarises the results of a complex experiment by image pair, by label or overall. A complementary measure of error, the overlap distance, is defined which captures the spatial extent of the nonoverlapping part and is related to the Hausdorff distance computed on grey level images. The generalized overlap measures are validated on synthetic images for which the overlap can be computed analytically and used as similarity measures in nonrigid registration of three-dimensional magnetic resonance imaging (MRI) brain images. Finally, a pragmatic segmentation ground truth is constructed by registering a magnetic resonance atlas brain to 20 individual scans, and used with the overlap measures to evaluate publicly available brain segmentation algorithms.

Phenomenological model of diffuse global and regional atrophy using finite-element methods.

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefacts is also presented. Cross-sectional and longitudinal sets of simulated data are shown and a visual classification protocol has been used by experts to rate real and simulated scans according to their degree of atrophy. Results confirm the potential of the proposed methodology.