Enhanced detection of cortical atrophy in Alzheimer's disease using structural MRI with anatomically constrained longitudinal registration.
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ABSTRACT:
SUBMITTER: Iannopollo E
PROVIDER: S-EPMC8249882 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Measurement of changes in brain cortical thickness is useful for the assessment of regional gray matter atrophy in neurodegenerative conditions. A new longitudinal method, called CLADA (cortical longitudinal atrophy detection algorithm), has been developed for the measurement of changes in cortical thickness in magnetic resonance images (MRI) acquired over time. CLADA creates a subject-specific cortical model which is longitudinally deformed to match images from individual time points. The algorithm was designed to work reliably for lower resolution images, such as the MRIs with 1×1×5 mm(3) voxels previously acquired for many clinical trials in multiple sclerosis (MS). CLADA was evaluated to determine reproducibility, accuracy, and sensitivity. Scan-rescan variability was 0.45% for images with 1mm(3) isotropic voxels and 0.77% for images with 1×1×5 mm(3) voxels. The mean absolute accuracy error was 0.43 mm, as determined by comparison of CLADA measurements to cortical thickness measured directly in post-mortem tissue. CLADA's sensitivity for correctly detecting at least 0.1mm change was 86% in a simulation study. A comparison to FreeSurfer showed good agreement (Pearson correlation=0.73 for global mean thickness). CLADA was also applied to MRIs acquired over 18 months in secondary progressive MS patients who were imaged at two different resolutions. Cortical thinning was detected in this group in both the lower and higher resolution images. CLADA detected a higher rate of cortical thinning in MS patients compared to healthy controls over 2 years. These results show that CLADA can be used for reliable measurement of cortical atrophy in longitudinal studies, even in lower resolution images.
| S-EPMC2965022 | biostudies-literature
Project description:This study identified structural and functional brain connectivity alterations in two independent samples of patients along the posterior cortical atrophy (PCA) disease course. Twenty-one PCA patients and 44 controls were recruited from two expert centres. Microstructural damage of white matter (WM) tracts was assessed using probabilistic tractography; resting state (RS) functional connectivity of brain networks was explored using a model free approach; grey matter (GM) atrophy was investigated using voxel-based morphometry. Compared with controls, common patterns of damage across PCA patients included: GM atrophy in the occipital-temporal-parietal regions; diffusion tensor (DT) MRI alterations of the corpus callosum and superior (SLF) and inferior longitudinal fasciculi (ILF) bilaterally; and decreased functional connectivity of the occipital gyri within the visual network and the precuneus and posterior cingulum within the default mode network (DMN). In PCA patients with longer disease duration and greater disease severity, WM damage extended to the cingulum and RS functional connectivity alterations spread within the frontal, dorsal attentive and salience networks. In PCA, reduced DMN functional connectivity was associated with SLF and ILF structural alterations. PCA patients showed distributed WM damage. Altered RS functional connectivity extends with disease worsening from occipital to temporo-parietal and frontostriatal regions, and this is likely to occur through WM connections. Future longitudinal studies are needed to establish trajectories of damage spreading in PCA and whether a combined DT MRI/RS functional MRI approach is promising in monitoring the disease progression.
| S-EPMC6019262 | biostudies-literature
Project description:Alzheimer's disease is characterized by cortical atrophy on MRI and abnormal depositions of amyloid-beta, phosphorylated-tau and inflammation pathologically. However, the relative contribution of these pathological hallmarks to cortical atrophy, a widely used MRI biomarker in Alzheimer's disease, is yet to be defined. Therefore, the aim of this study was to identify the histopathological correlates of MRI cortical atrophy in Alzheimer's disease donors, and its typical amnestic and atypical non-amnestic phenotypes. Nineteen Alzheimer's disease (of which 10 typical and 9 atypical) and 10 non-neurological control brain donors underwent post-mortem in situ 3T 3D-T1, from which cortical thickness was calculated with Freesurfer. Upon subsequent autopsy, 12 cortical brain regions from the right hemisphere and 9 from the left hemisphere were dissected and immunostained for amyloid-beta, phosphorylated-tau and reactive microglia, and percentage area load was calculated for each marker using ImageJ. In addition, post-mortem MRI was compared to ante-mortem MRI of the same Alzheimer's disease donors when available. MRI-pathology associations were assessed using linear mixed models. Higher amyloid-beta load weakly correlated with higher cortical thickness globally (r = 0.22, P = 0.022). Phosphorylated-tau strongly correlated with cortical atrophy in temporal and frontal regions (-0.76 < r < -1.00, all P < 0.05). Reactive microglia load strongly correlated with cortical atrophy in the parietal region (r = -0.94, P < 0.001). Moreover, post-mortem MRI scans showed high concordance with ante-mortem scans acquired <1 year before death. In conclusion, distinct histopathological markers differently correlated with cortical atrophy, highlighting their different roles in the neurodegenerative process, and therefore contributing to the understanding of the pathological underpinnings of MRI atrophic patterns in Alzheimer's disease. In our cohort, no or only subtle differences were found in MRI-pathology associations in Alzheimer's disease phenotypes, indicating that the histopathological correlates of cortical atrophy in typical and atypical phenotypes might be similar. Moreover, we show that post-mortem in situ MRI can be used as proxy for ante-mortem in vivo MRI.
| S-EPMC8677327 | biostudies-literature
Project description:Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.
| S-EPMC6588512 | biostudies-literature
Project description:ObjectivesTo investigate the global cortical and regional quantitative features of cortical neural architecture in the brains of patients with posterior cortical atrophy (PCA) and typical Alzheimer's disease (tAD) compared with elderly healthy controls (HC).MethodsA novel diffusion MRI method, that has been shown to correlate with minicolumnar organization changes in the cerebral cortex, was used as a surrogate of neuropathological changes in dementia. A cohort of 15 PCA patients, 23 tAD and 22 healthy elderly controls (HC) were enrolled to investigate the changes in cortical diffusivity among groups. For each subject, 3 T MRI T1-weighted images and diffusion tensor imaging (DTI) scans were analysed to extract novel cortical DTI derived measures (AngleR, PerpPD and ParlPD). Receiver operating characteristics (ROC) curve analysis and the area under the curve (AUC) were used to assess the group discrimination capability of the method.ResultsThe results showed that the global cortical DTI derived measures were able to detect differences, in both PCA and tAD patients compared to healthy controls. The AngleR was the best measure to discriminate HC from tAD (AUC?=?0.922), while PerpPD was the best measure to discriminate HC from PCA (AUC?=?0.961). Finally, the best global measure to differentiate the two patient groups was ParlPD (AUC?=?0.771). The comparison between PCA and tAD patients revealed a different pattern of damage within the AD spectrum and the regional comparisons identified significant differences in key regions including parietal and temporal lobe cortical areas. The best AUCs were shown by PerpPD right lingual cortex (AUC?=?0.856), PerpPD right superior parietal cortex (AUC?=?0.842) and ParlPD right lateral occipital cortex (AUC?=?0.826).ConclusionsDiagnostic group differences were found, suggesting that the new cortical DTI analysis method may be useful to investigate cortical changes in dementia, providing better characterization of neurodegeneration, and potentially aiding differential diagnosis and prognostic accuracy.
| S-EPMC7815619 | biostudies-literature
Project description:Modern tractography algorithms such as anatomically-constrained tractography (ACT) are based on segmentation maps of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). These maps are generally estimated from a T1-weighted (T1w) image and then registered in diffusion weighted images (DWI) space. Registration of T1w to diffusion space and partial volume estimation are challenging and rarely voxel-perfect. Diffusion-based segmentation would, thus, potentially allow not to have higher quality anatomical priors injected in the tractography process. On the other hand, even if FA-based tractography is possible without T1 registration, the literature shows that this technique suffers from multiple issues such as holes in the tracking mask and a high proportion of generated broken and anatomically implausible streamlines. Therefore, there is an important need for a tissue segmentation algorithm that works directly in the native diffusion space. We propose DORIS, a DWI-based deep learning segmentation algorithm. DORIS outputs 10 different tissue classes including WM, GM, CSF, ventricles, and 6 other subcortical structures (putamen, pallidum, hippocampus, caudate, amygdala, and thalamus). DORIS was trained and validated on a wide range of subjects, including 1,000 individuals from 22 to 90 years old from clinical and research DWI acquisitions, from 5 public databases. In the absence of a "true" ground truth in diffusion space, DORIS used a silver standard strategy from Freesurfer output registered onto the DWI. This strategy is extensively evaluated and discussed in the current study. Segmentation maps provided by DORIS are quantitatively compared to Freesurfer and FSL-fast and the impacts on tractography are evaluated. Overall, we show that DORIS is fast, accurate, and reproducible and that DORIS-based tractograms produce bundles with a longer mean length and fewer anatomically implausible streamlines.
| S-EPMC10406193 | biostudies-literature
Project description:Posterior cortical atrophy is a clinico-radiological syndrome characterized by progressive decline in visual processing and atrophy of posterior brain regions. With the majority of cases attributable to Alzheimer's disease and recent evidence for genetic risk factors specifically related to posterior cortical atrophy, the syndrome can provide important insights into selective vulnerability and phenotypic diversity. The present study describes the first major longitudinal investigation of posterior cortical atrophy disease progression. Three hundred and sixty-one individuals (117 posterior cortical atrophy, 106 typical Alzheimer's disease, 138 controls) fulfilling consensus criteria for posterior cortical atrophy-pure and typical Alzheimer's disease were recruited from three centres in the UK, Spain and USA. Participants underwent up to six annual assessments involving MRI scans and neuropsychological testing. We constructed longitudinal trajectories of regional brain volumes within posterior cortical atrophy and typical Alzheimer's disease using differential equation models. We compared and contrasted the order in which regional brain volumes become abnormal within posterior cortical atrophy and typical Alzheimer's disease using event-based models. We also examined trajectories of cognitive decline and the order in which different cognitive tests show abnormality using the same models. Temporally aligned trajectories for eight regions of interest revealed distinct (P < 0.002) patterns of progression in posterior cortical atrophy and typical Alzheimer's disease. Patients with posterior cortical atrophy showed early occipital and parietal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion leading to tissue loss of comparable extent later. Hippocampal, entorhinal and frontal regions underwent a lower rate of change and never approached the extent of posterior cortical involvement. Patients with typical Alzheimer's disease showed early hippocampal atrophy, with subsequent higher rates of temporal atrophy and ventricular expansion. Cognitive models showed tests sensitive to visuospatial dysfunction declined earlier in posterior cortical atrophy than typical Alzheimer's disease whilst tests sensitive to working memory impairment declined earlier in typical Alzheimer's disease than posterior cortical atrophy. These findings indicate that posterior cortical atrophy and typical Alzheimer's disease have distinct sites of onset and different profiles of spatial and temporal progression. The ordering of disease events both motivates investigation of biological factors underpinning phenotypic heterogeneity, and informs the selection of measures for clinical trials in posterior cortical atrophy.
| S-EPMC6598737 | biostudies-literature
Project description:PURPOSE:This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. PROCEDURES:Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. RESULTS:When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. CONCLUSIONS:The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.
| S-EPMC6374048 | biostudies-literature
Project description:ObjectiveDeficits in spatial navigation are characteristic and disabling features of typical Alzheimer's disease (tAD) and posterior cortical atrophy (PCA). Visual cues have been proposed to mitigate such deficits; however, there is currently little empirical evidence for their use.MethodsThe effect of visual cues on visually guided navigation was assessed within a simplified real-world setting in individuals with tAD (n = 10), PCA (n = 8), and healthy controls (n = 12). In a repeated-measures design comprising 36 trials, participants walked to a visible target destination (an open door within a built environment), with or without the presence of an obstacle. Contrast and motion-based cues were evaluated; both aimed to facilitate performance by applying perceptual changes to target destinations without carrying explicit information. The primary outcome was completion time; secondary outcomes were measures of fixation position and walking path directness during consecutive task phases, determined using mobile eyetracking and motion capture methods.ResultsResults illustrate marked deficits in patients' navigational ability, with patient groups taking an estimated two to three times longer to reach target destinations than controls and exhibiting tortuous walking paths. There were no significant differences between tAD and PCA task performance. Overall, patients took less time to reach target destinations under cue conditions (contrast-cue: 11.8%; 95% CI: [2.5, 20.3]) and were more likely initially to fixate on targets.InterpretationThe study evaluated navigation to destinations within a real-world environment. There is evidence that introducing perceptual changes to the environment may improve patients' navigational ability.
| S-EPMC5989777 | biostudies-literature
Project description:To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p?<?0.001) and first year visits (p?<?0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p?=?0.042), first year (p?=?0.028), and third year visits (p?=?0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.
| S-EPMC5841386 | biostudies-literature