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Magnetic Resonance Imaging Measurement of Entorhinal Cortex in the Diagnosis and Differential Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease.

ABSTRACT: Several magnetic resonance imaging studies have shown that the entorhinal cortex (ERC) is the first brain area related to pathologic changes in Alzheimer's disease (AD), even before atrophy of the hippocampus (HP). However, change in ERC morphology (thickness, surface area and volume) in the progression from aMCI to AD, especially in the subtypes of aMCI (single-domain and multiple-domain: aMCI-s and aMCI-m), however, is still unclear. ERC thickness, surface area and volume were measured in 29 people with aMCI-s, 22 people with aMCI-m, 18 patients with AD and 26 age-/sex-matched healthy controls. Group comparisons of the ERC geometry measurements (including thickness, volume and surface area) were performed using analyses of covariance (ANCOVA). Furthermore, receiver operator characteristic (ROC) analyses and the area under the curve (AUC) were employed to investigate classification ability (HC, aMCI-s, aMCI-m and AD from each other). There was a significant decreasing tendency in ERC thickness from HC to aMCI-s to aMCI-m to finally AD in both the left and the right hemispheres (left hemisphere: HC > aMCI-s > AD; right hemisphere: aMCI-s > aMCI-m > AD). For ERC volume, both the AD group and the aMCI-m group showed significantly decreased volume on both sides compared with the HC group. In addition, the AD group also had significantly decreased volume on both sides compared with the aMCI-s group. As for the ERC surface area, no significant difference was identified among the four groups. Furthermore, the AUC results demonstrate that combined ERC parameters (thickness and volume) can better discriminate the four groups from each other than ERC thickness alone. Finally, and most importantly, relative to HP volume, the capacity of combined ERC parameters was better at discriminating between HC and aMCI-s, as well as aMCI-m and AD. ERC atrophy, particularly the combination of ERC thickness and volume, might be regarded as a promising candidate biomarker in the diagnosis and differential diagnosis of aMCI and AD.

SUBMITTER: Li Q

PROVIDER: S-EPMC8471837 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The entorhinal cortex is one of the first regions to exhibit neurodegeneration in Alzheimer's disease, and as such identification of entorhinal cortex dysfunction may aid detection of the disease in its earliest stages. Extensive evidence demonstrates that the entorhinal cortex is critically implicated in navigation underpinned by the firing of spatially modulated neurons. This study tested the hypothesis that entorhinal-based navigation is impaired in pre-dementia Alzheimer's disease. Forty-five patients with mild cognitive impairment (26 with CSF Alzheimer's disease biomarker data: 12 biomarker-positive and 14 biomarker-negative) and 41 healthy control participants undertook an immersive virtual reality path integration test, as a measure of entorhinal-based navigation. Behavioural performance was correlated with MRI measures of entorhinal cortex volume, and the classification accuracy of the path integration task was compared with a battery of cognitive tests considered sensitive and specific for early Alzheimer's disease. Biomarker-positive patients exhibited larger errors in the navigation task than biomarker-negative patients, whose performance did not significantly differ from controls participants. Path-integration performance correlated with Alzheimer's disease molecular pathology, with levels of CSF amyloid-β and total tau contributing independently to distance error. Path integration errors were negatively correlated with the volumes of the total entorhinal cortex and of its posteromedial subdivision. The path integration task demonstrated higher diagnostic sensitivity and specificity for differentiating biomarker positive versus negative patients (area under the curve = 0.90) than was achieved by the best of the cognitive tests (area under the curve = 0.57). This study demonstrates that an entorhinal cortex-based virtual reality navigation task can differentiate patients with mild cognitive impairment at low and high risk of developing dementia, with classification accuracy superior to reference cognitive tests considered to be highly sensitive to early Alzheimer's disease. This study provides evidence that navigation tasks may aid early diagnosis of Alzheimer's disease, and the basis of this in animal cellular and behavioural studies provides the opportunity to answer the unmet need for translatable outcome measures for comparing treatment effect across preclinical and clinical trial phases of future anti-Alzheimer's drugs.

Project description:BackgroundTextural features of the hippocampus in structural magnetic resonance imaging (sMRI) images can serve as potential diagnostic biomarkers for Alzheimer's disease (AD), while exhibiting a relatively poor discriminant performance in detecting early AD, such as amnestic mild cognitive impairment (aMCI). In contrast to sMRI, functional magnetic resonance imaging (fMRI) can identify brain functional abnormalities in the early stages of cerebral disorders. However, whether the textural features reflecting local functional activity in the hippocampus can improve the diagnostic performance for AD and aMCI remains unclear. In this study, we combined the textural features of the amplitude of low frequency fluctuation (ALFF) in the slow-5 frequency band and structural images in the hippocampus to investigate their diagnostic performance for AD and aMCI using multimodal radiomics technique.MethodsTotally, 84 AD, 50 aMCI, and 44 normal controls (NCs) were included in the current study. After feature extraction and feature selection, the radiomics models incorporating sMRI images, ALFF values and their combinations in the bilateral hippocampus were established for the diagnosis of AD and aMCI. The effectiveness of these models was evaluated by receiver operating characteristic (ROC) analysis. The radiomics models were further validated using the external data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.ResultsThe results of ROC analysis showed that the radiomics models based on structural images in the hippocampus had a better diagnostic performance for AD compared with the models using ALFF, while the ALFF-based model exhibited better discriminant performance for aMCI than the models with structural images. The radiomics models based on the combinations of structural images and ALFF were found to exhibit the highest accuracy for distinguishing AD from NCs and aMCI from NCs.ConclusionIn this study, we found that the textural features reflecting local functional activity could improve the diagnostic performance of traditional structural models for both AD and aMCI. These findings may deepen our understanding of the pathogenesis of AD, contributing to the early diagnosis of AD.

Project description:PurposeTo detect changes in brain lateralization in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) using resting-state functional magnetic resonance imaging (fMRI).Materials and methodsData from 61 well-matched right-handed subjects were obtained from the Alzheimer's Disease Neuroimaging Initiative, including 19 healthy controls (HCs), 25 patients with MCI, and 17 patients with AD. First, we divided 256 pairs of seed regions from each hemisphere covering the entire cerebral gray matter. Then, we used the intrinsic laterality index (iLI) approach to quantify the functional laterality using fMRI. One-way ANOVA was employed to estimate the differences in iLI among the three groups. The sum, number and mean value of the iLI were calculated within the thresholds of 0?<?|iLI|?<?0.2, 0.2???|iLI|?<?0.4, 0.4???|iLI|?<?0.8, and |iLI|???0.8, to explore the changes in the lateralization of resting-state brain function in patients with MCI and AD.ResultsOne-way ANOVA revealed that the iLIs of the three groups were significantly different. The HCs showed a significant leftward interhemispheric difference within |iLI|???0.8. Compared with the HCs, the patients with MCI manifested a distinct abnormal rightward interhemispheric asymmetry, mainly within the thresholds of 0.2???|iLI|?<?0.4 and 0.4???|iLI|?<?0.8; in the patients with AD, the normal leftward lateralization that was observed in the HCs disappeared, and an abnormal rightward laterality was expressed within 0.4???|iLI|?<?0.8. By directly comparing the patients with MCI with the patients with AD, an exclusive abnormal rightward laterality was observed in the patients with MCI within the 0.2???|iLI|?<?0.4 threshold, and the normal leftward asymmetry vanished in the patients with AD within the |iLI|???0.8 threshold.ConclusionGlobal brain lateralization was different among three groups. The abnormal rightward dominance observed in the patients with MCI and AD may indicate that these patients use additional brain resources to compensate for the loss of cognitive function, and the observed disappearance of the leftward laterality in the patients with AD was likely associated with the damage in the left hemisphere. The observed disappearance of the rightward asymmetry in the patients with AD using the 0.2???|iLI|?<?0.4 threshold was likely a sign of decompensation. Our study provides new insights that may improve our understanding of MCI and AD.

Project description:BackgroundPhase-contrast magnetic resonance imaging (PC-MRI) enables quantification of cerebrospinal fluid (CSF) flow and total cerebral blood (tCBF) flow and may be of value for the etiological diagnosis of neurodegenerative diseases. This investigation aimed to study CSF flow and intracerebral vascular flow in patients with Alzheimer's disease (AD) and patients with amnesic mild cognitive impairment (a-MCI) and to compare the results with patients with idiopathic normal pressure hydrocephalus (NPH) and with healthy elderly volunteers (HEV).MethodsTen a-MCI and 9 mild AD patients were identified in a comprehensive neurological and neuropsychological assessment. They underwent brain MRI; PC-MRI pulse sequence was performed with the following parameters: two views per segment; flip angle: 25° for vascular flow and 20° for CSF flow; field-of-view (FOV): 14 × 14 mm²; matrix: 256 × 128; slice thickness: 5 mm; with one excitation for exams on the 3 T machine, and 2 excitations for the 1.5 T machine exams. Velocity (encoding) sensitization was set to 80 cm/s for the vessels at the cervical level, 10 or 20 cm/s for the aqueduct and 5 cm/s for the cervical subarachnoid space (SAS). Dynamic flow images were analyzed with in-house processing software. The patients' results were compared with those obtained for HEVs (n = 12), and for NPH patients (n = 13), using multivariate analysis.ResultsArterial tCBF and the calculated pulsatility index were significantly greater in a-MCI patients than in HEVs. In contrast, vascular parameters were lower in NPH patients. Cervical CSF flow analysis yielded similar values for all four populations. Aqueductal CSF stroke volumes (in μl per cardiac cycle) were similar in HEVs (34 ± 17) and AD patients (39 ± 18). In contrast, the aqueductal CSF was hyperdynamic in a-MCI patients (73 ± 33) and even more so in NPH patients (167 ± 89).ConclusionOur preliminary data show that a-MCI patients present with high systolic arterial peak flows, which are associated with higher mean total cerebral arterial flows. Aqueductal CSF oscillations are within normal range in AD and higher than normal in NPH. This study provides an original dynamic vision of cerebral neurodegenerative diseases, consistent with the vascular theory for AD, and supporting primary flow disturbances different from those observed in NPH.

Project description:BACKGROUND:Quantitative T2 and diffusion MRI indices inform about tissue state and microstructure, both of which may be affected by pathology before tissue atrophy. PURPOSE:To evaluate the capability of both volumetric and quantitative MRI (qMRI) of the hippocampus and entorhinal cortex (EC) for classification of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease dementia (ADD). STUDY TYPE:Retrospective cross-sectional study. POPULATION:Consecutive cohorts of healthy age-matched controls (n?=?62), aMCI patients (n?=?25), and ADD patients (n?=?14). FIELD STRENGTH/SEQUENCE:3T using T1-weighted imaging, T2-weighted imaging, T2 relaxometry and diffusion tensor imaging (DTI). ASSESSMENT:Montreal Cognitive Assessment and paired associate learning tests for cognitive state. Hippocampal subfield volumes by the automated segmentation of hippocampal subfields system from structural brain images. T2 relaxation time and DTI indices quantified for hippocampal subfields. The fraction of voxels with high T2 values (>20 ms above subfield median) was calculated and regionalized for hippocampus and EC. STATISTICAL TESTS:Support vector machine and receiver operating characteristic analyses from cognitive and MRI data. RESULTS:qMRI classified aMCI and ADD with excellent sensitivity (79.0% and 94.5%, respectively) and specificity (85.6% and 86.1%, respectively), superior to volumes alone (70.0% and 84.5% for respective sensitivities; 82.2 and 91.1 for respective specificities) and similar to cognitive tests (61.7% and 87.5% for respective sensitivities; 88.2% and 90.7% for respective specificities). Regions of high T2 are dispersed throughout each hippocampal subfield in aMCI and ADD with higher concentration than controls, and was most pronounced in the EC. No other individual qMRI marker than EC volume can separate aMCI from ADD, however. DATA CONCLUSION: qMRI markers of hippocampal and entorhinal tissue states are sensitive and specific classifiers of aMCI and ADD. They may serve as markers of a neurodegenerative state preceding volume loss. LEVEL OF EVIDENCE:2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:445-455.

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Magnetic Resonance Imaging Measurement of Entorhinal Cortex in the Diagnosis and Differential Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease.

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