The ERICA Score: An MR Imaging-based Visual Scoring System for the Assessment of Entorhinal Cortex Atrophy in Alzheimer Disease

Towards validation in clinical routine: a comparative analysis of visual MTA ratings versus the automated ratio between inferior lateral ventricle and hippocampal volumes in Alzheimer's disease diagnosis

PURPOSE

To assess the performance of the inferior lateral ventricle (ILV) to hippocampal (Hip) volume ratio on brain MRI, for Alzheimer's disease (AD) diagnostics, comparing it to individual automated ILV and hippocampal volumes, and visual medial temporal lobe atrophy (MTA) consensus ratings.

METHODS

One-hundred-twelve subjects (mean age ± SD, 66.85 ± 13.64 years) with varying degrees of cognitive decline underwent MRI using a Philips Ingenia 3T. The MTA scale by Scheltens, rated on coronal 3D T1-weighted images, was determined by three experienced radiologists, blinded to diagnosis and sex. Automated volumetry was computed by icobrain dm (v. 5.10) for total, left, right hippocampal, and ILV volumes. The ILV/Hip ratio, defined as the percentage ratio between ILV and hippocampal volumes, was calculated and compared against a normative reference population (n = 1903). Inter-rater agreement, association, classification accuracy, and clinical interpretability on patient level were reported.

RESULTS

Visual MTA scores showed excellent inter-rater agreement. Ordinal logistic regression and correlation analyses demonstrated robust associations between automated brain segmentations and visual MTA ratings, with the ILV/Hip ratio consistently outperforming individual hippocampal and ILV volumes. Pairwise classification accuracy showed good performance without statistically significant differences between the ILV/Hip ratio and visual MTA across disease stages, indicating potential interchangeability. Comparison to the normative population and clinical interpretability assessments showed commensurability in classifying MTA "severity" between visual MTA and ILV/Hip ratio measurements.

CONCLUSION

The ILV/Hip ratio shows the highest correlation to visual MTA, in comparison to automated individual ILV and hippocampal volumes, offering standardized measures for diagnostic support in different stages of cognitive decline.

Comparison of Quantitative Hippocampal Volumes and Structured Scoring Scales in Predicting Alzheimer Disease Diagnosis

BACKGROUND AND PURPOSE

Brain imaging plays an important role in investigating patients with cognitive decline and ruling out secondary causes of dementia. This study compares the diagnostic value of quantitative hippocampal volumes derived from automated volumetric software and structured scoring scales in differentiating Alzheimer disease, mild cognitive impairment, and subjective cognitive decline.

MATERIALS AND METHODS

Retrospectively, we reviewed images and medical records of adult patients who underwent MR imaging with a dementia protocol (2018-2021). Patients with postscanning diagnoses of Alzheimer disease, mild cognitive impairment, and subjective cognitive decline based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision, were included. Diagnostic performances of automated normalized total hippocampal volume and structured manually assigned medial temporal atrophy and entorhinal cortical atrophy scores were assessed using multivariate logistic regression and receiver operating characteristic curve analysis.

RESULTS

We evaluated 328 patients (Alzheimer disease, n = 118; mild cognitive impairment, n = 172; subjective cognitive decline, n = 38). Patients with Alzheimer disease had lower normalized total hippocampal volume (median, 0.35%), higher medial temporal atrophy (median, 3), and higher entorhinal cortical atrophy (median, 2) scores than those with subjective cognitive decline (P < .001) and mild cognitive impairment (P < .001). For discriminating Alzheimer disease from subjective cognitive decline, an entorhinal cortical atrophy cutoff value of 2 had a higher specificity (87%) compared with normalized total hippocampal volume (74%) and medial temporal atrophy (66%), but a lower sensitivity (69%) than normalized total hippocampal volume (84%) and medial temporal atrophy (84%). In discriminating Alzheimer disease from mild cognitive impairment, an entorhinal cortical atrophy cutoff value of 3 had a specificity (66%), similar to that of normalized total hippocampal volume (67%) but higher than medial temporal atrophy (54%), and its sensitivity (69%) was also similar to that of normalized total hippocampal volume (71%) but lower than that of medial temporal atrophy (84%).

CONCLUSIONS

Entorhinal cortical atrophy and medial temporal atrophy may be useful adjuncts in discriminating Alzheimer disease from subjective cognitive decline, with reduced cost and implementation challenges compared with automated volumetric software.

Influence of cerebral blood flow on volumetric loss related to Alzheimer's disease

CBF measured with Arterial Spin Labeling (ASL) obtained by Magnetic Resonance Imaging (MRI) may become an important biomarker by showing changes in early stages of AD, such as in the prodromal stage of Mild Cognitive Impairment (MCI).

Objective

Verify the correlation between atrophy and CBF in patients with MCI and mild phase ADD, to demonstrate whether changes in CBF can be considered as vascular biomarkers in the diagnosis of the DA continuum.

Methods

11 healthy volunteers, 16 MCI and 15 mild ADD were evaluated. Images of the brain were acquired, including CBF measured with Arterial Spin Labeling (ASL).

Results

When comparing MCI with control, a reduction in normalized CBF was observed in left posterior cingulate (estimated difference -0.38; p=0.02), right posterior cingulate (estimated difference -0.45; p=0.02) and right precuneus (estimated difference -0.28; p <0.01); also increase in normalized CBF in right upper temporal pole (estimated difference 0.22; p=0.03). It was also observed that in MCI, the smaller the gray matter volume, the smaller the CBF in the left posterior cingulate; as well as the greater the cerebrospinal fluid volume, consequent to the encephalic volumetric reduction associated with atrophy, the greater the CBF in the right superior temporal pole. When comparing controls, MCI and mild AD, in relation to the other variables, no other correlations were observed between CBF and atrophy.

Conclusion

In patients with MCI, the reduction of CBF in the left posterior cingulate correlated with gray matter atrophy, as well as the increase of CBF in the right upper temporal pole correlated with an increase in cerebrospinal fluid consequent to the encephalic volumetric reduction associated with atrophy, demonstrating the influence of CBF in AD related brain atrophy. These findings position CBF as a possible vascular biomarker for early-stage AD diagnoses.

Frontal and temporal lobe correlates of verbal learning and memory in aMCI and suspected Alzheimer's disease dementia

Alzheimer's disease is primarily known for deficits in learning and retaining new information. This has long been associated with pathological changes in the mesial temporal lobes. The role of the frontal lobes in memory in Alzheimer's disease is less well understood. In this study, we examined the role of the frontal lobes in learning, recognition, and retention of new verbal information, as well as the presence of specific errors (i.e., intrusions and false-positive errors). Participants included one hundred sixty-seven patients clinically diagnosed with amnestic mild cognitive impairment or suspected Alzheimer's disease dementia who were administered the California Verbal Learning Test and completed high-resolution MRI. We confirmed the role of the mesial temporal lobes in learning and retention, including the volumes of the hippocampus, entorhinal cortex, and parahippocampal gyrus. In addition, false-positive errors were associated with all volumes of the mesial temporal lobes and widespread areas within the frontal lobes. Errors of intrusion were related to the supplementary motor cortex and hippocampus. Most importantly, the mesial temporal lobes interacted with the frontal lobes for learning, recognition, and memory errors. Lower volumes in both regions explained more performance variance than any single structure. This study supports the interaction of the frontal lobes with the temporal lobes in many aspects of memory in Alzheimer's disease.

Volume atrophy in medial temporal cortex and verbal memory scores in American Indians: Data from the Strong Heart Study

INTRODUCTION

Distinguishing Alzheimer's disease (AD) patient subgroups may optimize positive clinical outcomes. Cortical atrophy is correlated with memory deficits, but these associations are understudied in American Indians.

METHODS

We collected imaging and cognition data in the Strong Heart Study (SHS), a cohort of 11 tribes across three regions. We processed 1.5T MRI using FreeSurfer and iterative principal component analysis. Linear mixed models estimated volumetric associations with diabetes.

RESULTS

Over mean 7 years follow-up (N = 818 age 65-89 years), overall volume loss was 0.5% per year. Significant losses associated with diabetes were especially strong in the right hemisphere. Annualized hippocampal, parahippocampal, entorhinal atrophy were worse for men, older age, diabetes, hypertension, stroke; and associated with both encoding and retrieval memory losses.

DISCUSSION

Our findings suggest that diabetes is an important risk factor in American Indians for cortical atrophy and memory loss. Future research should examine opportunities for primary prevention in this underserved population.

Excellent Interrater Reliability for Manual Segmentation of the Medial Perirhinal Cortex

Objective: Evaluation of interrater reliability for manual segmentation of brain structures that are affected first by neurofibrillary tau pathology in Alzheimer's disease. Method: Medial perirhinal cortex, lateral perirhinal cortex, and entorhinal cortex were manually segmented by two raters on structural magnetic resonance images of 44 adults (20 men; mean age = 69.2 ± 10.4 years). Intraclass correlation coefficients (ICC) of cortical thickness and volumes were calculated. Results: Very high ICC values of manual segmentation for the cortical thickness of all regions (0.953-0.986) and consistently lower ICC values for volume estimates of the medial and lateral perirhinal cortex (0.705-0.874). Conclusions: The applied manual segmentation protocol allows different raters to achieve remarkably similar cortical thickness estimates for regions of the parahippocampal gyrus. In addition, the results suggest a preference for cortical thickness over volume as a reliable measure of atrophy, especially for regions affected by collateral sulcus variability (i.e., medial and lateral perirhinal cortex). The results provide a basis for future automated segmentation and collection of normative data.

Visual atrophy rating scales and amyloid PET status in an Alzheimer's disease clinical cohort

OBJECTIVES

Visual rating scales (VRS) are the quantification method closest to the approach used in routine clinical practice to assess brain atrophy. Previous studies have suggested that the medial temporal atrophy (MTA) rating scale is a reliable diagnostic marker for AD, equivalent to volumetric quantification, while others propose a higher diagnostic utility for the Posterior Atrophy (PA) scale in early-onset AD.

METHODS

Here, we reviewed 14 studies that assessed the diagnostic accuracy of PA and MTA, we explored the issue of cut-off heterogeneity, and assessed 9 rating scales in a group of patients with biomarker-confirmed diagnosis. A neuroradiologist blinded to all clinical information rated the MR images of 39 amyloid-positive and 38 amyloid-negative patients using 9 validated VRS assessing multiple brain regions. Automated volumetric analyses were performed on a subset of patients (n = 48) and on a group of cognitively normal individuals (n = 28).

RESULTS

No single VRS could differentiate amyloid-positive from amyloid-negative patients with other neurodegenerative conditions. 44% of amyloid-positive patients were deemed to have age-appropriate levels of MTA. In the amyloid-positive group, 18% had no abnormal MTA or PA scores. These findings were substantially affected by cut-off selection. Amyloid-positive and amyloid-negative patients had comparable hippocampal and parietal volumes, and MTA but not PA scores correlated with the respective volumetric measures.

INTERPRETATION

Consensus guidelines are needed before VRS can be recommended for use in the diagnostic workup of AD. Our data are suggestive of high intragroup variability and non-superiority of volumetric quantification of atrophy over visual assessment.

Association Between Hippocampal Volume and African Neuropsychology Memory Tests in Adult Individuals with Probable Alzheimer's Disease in Democratic Republic of Congo

BACKGROUND

Western studies indicate potential associations between hippocampal volume and memory in the trajectory of Alzheimer's disease (AD). However, limited availability of neuroimaging technology and neuropsychological tests appropriate for sub-Saharan African (SSA) countries makes it difficult to establish neuroanatomical associations of hippocampus and memory in this locale.

OBJECTIVE

This study examined hippocampal volumes and memory in healthy control (HC) and probable AD groups in the Democratic Republic of Congo (DRC).

METHODS

Forty-six subjects with probable AD and 29 HC subjects were screened using the Community Instrument for Dementia and the Alzheimer Questionnaire. Participants underwent neuroimaging in Kinshasa, DRC, and memory was evaluated using the African Neuropsychology Battery (ANB). Multiple linear regression was used to determine associations between hippocampal volumes and memory.

RESULTS

Patients with probable AD performed significantly worse than HCs on ANB memory measures, and exhibited greater cerebral atrophy, which was significantly pronounced in the medial temporal lobe region (hippocampus, entorhinal cortex). Both AD and HC subjects exhibited high rates of white matter hyperintensities compared to international base rate prevalence, which was significantly worse for probable AD. Both also exhibited elevated rates of microhemorrhages. Regression analysis demonstrated a significant association between hippocampal volume and ANB memory tests. Hippocampal atrophy discriminated probable AD from the HC group.

CONCLUSIONS

This study establishes the feasibility of conducting neuroimaging research in the SSA, demonstrates many known neuroimaging findings in probable AD patients hold up using culturally appropriate memory tasks, and suggest cardiovascular problems are a greater issue in SSA than in Western countries.

Specificity of Entorhinal Atrophy MRI Scale in Predicting Alzheimer's Disease Conversion

We compared entorhinal cortex atrophy (ERICA) score vs. medial temporal atrophy (MTA) score's ability to predict conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) using magnetic resonance imaging (MRI). We hypothesized that ERICA would show higher specificity. Data from 61 aMCI patients were analyzed. Positive ERICA was associated with AD conversion with a sensitivity of 56% (95% CI: 30-80%) and a specificity of 78% (63-89%) vs. 69% (41-89%) SE and 60% (44-74%) SP for the MTA. Results suggest that ERICA is superior to MTA in predicting conversion from aMCI to AD in a small sample of participants.

Diagnostic Tools for Alzheimer's Disease: A Narrative Review Based on Our Own Research Experience

Alzheimer's disease (AD), one of the most representative neurodegenerative diseases, has diverse neurobiological and pathophysiological mechanisms. Treatment strategies targeting a single mechanism have repeated faced failures because the mechanism of neuronal cell death is very complex that is not fully understood yet. Since complex mechanisms exist to explain AD, a variety of diagnostic biomarkers for diagnosing AD are required. Moreover, standardized evaluations for comprehensive diagnosis using neuropsychological, imaging, and laboratory tools are needed. In this review, we summarize the latest clinical, neuropsychological, imaging, and laboratory evaluations to diagnose patients with AD based on our own experience in conducting a prospective study.

Medial Temporal Lobe Anatomy

The medial temporal lobe (MTL) is a complex anatomic region encompassing the hippocampal formation, parahippocampal region, and amygdaloid complex. To enable the reader to understand the well-studied regional anatomic relationships and cytoarchitecture that form the basis of functional connectivity, the authors have created a detailed yet approachable anatomic reference for clinicians and scientists, with special attention to MR imaging. They have focused primarily on the hippocampal formation, discussing its gross structural features, anatomic relationships, and subfield anatomy and further discuss hippocampal terminology and development, hippocampal connectivity, normal anatomic variants, clinically relevant disease processes, and automated hippocampal segmentation software.

Diagnostic performance of hippocampal volumetry in Alzheimer's disease or mild cognitive impairment: a meta-analysis

OBJECTIVE

To evaluate the diagnostic performance of hippocampal volumetry for Alzheimer's disease (AD) or mild cognitive impairment (MCI).

METHODS

The MEDLINE and Embase databases were searched for articles that evaluated the diagnostic performance of hippocampal volumetry in differentiating AD or MCI from normal controls, published up to March 6, 2022. The quality of the articles was evaluated by the QUADAS-2 tool. A bivariate random-effects model was used to pool sensitivity, specificity, and area under the curve. Sensitivity analysis and meta-regression were conducted to explain study heterogeneity. The diagnostic performance of entorhinal cortex volumetry was also pooled.

RESULTS

Thirty-three articles (5157 patients) were included. The pooled sensitivity and specificity for AD were 82% (95% confidence interval [CI], 77-86%) and 87% (95% CI, 82-91%), whereas those for MCI were 60% (95% CI, 51-69%) and 75% (95% CI, 67-81%), respectively. No difference in the diagnostic performance was observed between automatic and manual segmentation (p = 0.11). MMSE scores, study design, and the reference standard being used were associated with study heterogeneity (p < 0.01). Subgroup analysis demonstrated a higher diagnostic performance of entorhinal cortex volumetry for both AD (pooled sensitivity: 88% vs. 79%, specificity: 92% vs. 89%, p = 0.07) and MCI (pooled sensitivity: 71% vs. 55%, specificity: 83% vs. 68%, p = 0.06).

CONCLUSIONS

Our meta-analysis demonstrated good diagnostic performance of hippocampal volumetry for AD or MCI. Entorhinal cortex volumetry might have superior diagnostic performance to hippocampal volumetry. However, due to a small number of studies, the diagnostic performance of entorhinal cortex volumetry is yet to be determined.

KEY POINTS

• The pooled sensitivity and specificity of hippocampal volumetry for Alzheimer's disease were 82% and 87%, whereas those for mild cognitive impairment were 60% and 75%, respectively. • No significant difference in the diagnostic performance was observed between automatic and manual segmentation. • Subgroup analysis demonstrated superior diagnostic performance of entorhinal cortex volumetry for AD (pooled sensitivity: 88%, specificity: 92%) and MCI (pooled sensitivity: 71%, specificity: 83%).

Imaging Aspects of the Hippocampus

The hippocampus is one of the most sophisticated structures in the brain, owing to its complex anatomy, intriguing functions, relationship with other structures, and relevant associated symptoms. Despite being a structure analyzed for centuries, its anatomy and physiology in the human body are still being extensively studied, as well as associated pathologic conditions and potential biomarkers. It can be affected by a broad group of diseases that can be classified as congenital, degenerative, infectious or inflammatory, neoplastic, vascular, or toxic-metabolic disease. The authors present the anatomy and close structures, function, and development of the hippocampus, as well as an original algorithm for imaging diagnosis. The algorithm includes pathologic conditions that typically affect the hippocampus and groups them into nodular (space occupying) and nonnodular pathologic conditions, serving as a guide to narrow the differential diagnosis. MRI is the imaging modality of choice for evaluation of the hippocampus, and CT and nuclear medicine also improve the analysis. The MRI differential diagnosis depends on anatomic recognition and careful characterization of associated imaging findings such as volumetric changes, diffusion restriction, cystic appearance, hyperintensity at T1-weighted imaging, enhancement, or calcification, which play a central role in diagnosis along with clinical findings. Some pathologic conditions arising from surrounding structures such as the amygdala are also important to recognize. Pathologic conditions of the hippocampus can be a challenge to diagnose because they usually manifest as similar clinical syndromes, so the imaging findings play a potential role in guiding the final diagnosis. Online supplemental material is available for this article. ^©RSNA, 2022.

[Brain MRI-Based Artificial Intelligence Software in Patients with Neurodegenerative Diseases: Current Status]

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

Imaging biomarkers for Alzheimer's disease and glaucoma: Current and future practices

Glaucoma is a leading cause of blindness worldwide. Although intraocular pressure is the main risk factor for glaucoma, several intraocular pressure independent factors have been associated with the risk of developing the disease and its progression. The diagnosis of glaucoma relies on clinical features of the optic nerve, visual field test, and optical coherence tomography. However, the multidisciplinary aspect of the disease suggests that other biomarkers may be useful for the diagnosis, thus underling the importance of novel imaging techniques supporting clinicians. This review analyzes the common pathogenic mechanisms between glaucoma and Alzheimer's disease and the possible novel approaches for diagnosis and follow up.

Diagnostic performance of the medial temporal lobe atrophy scale in patients with Alzheimer's disease: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic performance and reliability of the medial temporal lobe atrophy (MTA) scale in patients with Alzheimer's disease.

METHODS

A systematic literature search of MEDLINE and EMBASE databases was performed to select studies that evaluated the diagnostic performance or reliability of MTA scale, published up to January 21, 2021. Pooled estimates of sensitivity and specificity were calculated using a bivariate random-effects model. Pooled correlation coefficients for intra- and interobserver agreements were calculated using the random-effects model based on Fisher's Z transformation of correlations. Meta-regression was performed to explain the study heterogeneity. Subgroup analysis was performed to compare the diagnostic performance of the MTA scale and hippocampal volumetry.

RESULTS

Twenty-one original articles were included. The pooled sensitivity and specificity of the MTA scale in differentiating Alzheimer's disease from healthy control were 74% (95% CI, 68-79%) and 88% (95% CI, 83-91%), respectively. The area under the curve of the MTA scale was 0.88 (95% CI, 0.84-0.90). Meta-regression demonstrated that the difference in the method of rating the MTA scale was significantly associated with study heterogeneity (p = 0.04). No significant difference was observed in five studies regarding the diagnostic performance between MTA scale and hippocampal volumetry (p = 0.40). The pooled correlation coefficients for intra- and interobserver agreements were 0.85 (95% CI, 0.69-0.93) and 0.83 (95% CI, 0.66-0.92), respectively.

CONCLUSIONS

Our meta-analysis demonstrated a good diagnostic performance and reliability of the MTA scale in Alzheimer's disease.

KEY POINTS

• The pooled sensitivity and specificity of the MTA scale in differentiating Alzheimer's disease from healthy control were 74% and 88%, respectively. • There was no significant difference in the diagnostic performance between MTA scale and hippocampal volumetry. • The reliability of MTA scale was excellent based on the pooled correlation coefficient for intra- and interobserver agreements.

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

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.

Long-term microstructure and cerebral blood flow changes in patients recovered from COVID-19 without neurological manifestations

BACKGROUNDThe coronavirus disease 2019 (COVID-19) rapidly progressed to a global pandemic. Although some patients totally recover from COVID-19 pneumonia, the disease's long-term effects on the brain still need to be explored.METHODSWe recruited 51 patients with 2 subtypes of COVID-19 (19 mild and 32 severe) with no specific neurological manifestations at the acute stage and no obvious lesions on the conventional MRI 3 months after discharge. Changes in gray matter morphometry, cerebral blood flow (CBF), and white matter (WM) microstructure were investigated using MRI. The relationship between brain imaging measurements and inflammation markers was further analyzed.RESULTSCompared with healthy controls, the decrease in cortical thickness/CBF and the changes in WM microstructure were more severe in patients with severe disease than in those with mild disease, especially in the frontal and limbic systems. Furthermore, changes in brain microstructure, CBF, and tract parameters were significantly correlated (P < 0.05) with the inflammatory markers C-reactive protein, procalcitonin, and interleukin 6.CONCLUSIONIndirect injury related to inflammatory storm may damage the brain, altering cerebral volume, CBF, and WM tracts. COVID-19-related hypoxemia and dysfunction of vascular endothelium may also contribute to neurological changes. The abnormalities in these brain areas need to be monitored during recovery, which could help clinicians understand the potential neurological sequelae of COVID-19.FUNDINGNatural Science Foundation of China.

Multiparametric magnetic resonance imaging and positron emission tomography findings in neurodegenerative diseases: Current status and future directions

Neurodegenerative diseases (NDDs) are characterized by progressive neuronal loss, leading to dementia and movement disorders. NDDs broadly include Alzheimer's disease, frontotemporal lobar degeneration, parkinsonian syndromes, and prion diseases. There is an ever-increasing prevalence of mild cognitive impairment and dementia, with an accompanying immense economic impact, prompting efforts aimed at early identification and effective interventions. Neuroimaging is an essential tool for the early diagnosis of NDDs in both clinical and research settings. Structural, functional, and metabolic imaging modalities, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are widely available. They show encouraging results for diagnosis, monitoring, and treatment response evaluation. The current review focuses on the complementary role of various imaging modalities in relation to NDDs, the qualitative and quantitative utility of newer MRI techniques, novel radiopharmaceuticals, and integrated PET/MRI in the setting of NDDs.

Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images

BACKGROUND AND PURPOSE

Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning-based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images.

MATERIALS AND METHODS

A deep learning-based algorithm was developed using a dataset of T1-weighted brain MR images in consecutive patients with Alzheimer disease and mild cognitive impairment. We developed a 2-step algorithm using a convolutional neural network to perform brain parcellation followed by 3 classifier techniques including XGBoost for disease prediction. All classification experiments were performed using 5-fold cross-validation. The diagnostic performance of the XGBoost method was compared with logistic regression and a linear Support Vector Machine by calculating their areas under the curve for differentiating Alzheimer disease from mild cognitive impairment and mild cognitive impairment from healthy controls.

RESULTS

In a total of 4 datasets, 1099, 212, 711, and 705 eligible patients were included. Compared with the linear Support Vector Machine and logistic regression, XGBoost significantly improved the prediction of Alzheimer disease (P < .001). In terms of differentiating Alzheimer disease from mild cognitive impairment, the 3 algorithms resulted in areas under the curve of 0.758-0.825. XGBoost had a sensitivity of 68% and a specificity of 70%. In terms of differentiating mild cognitive impairment from the healthy control group, the 3 algorithms resulted in areas under the curve of 0.668-0.870. XGBoost had a sensitivity of 79% and a specificity of 80%.

CONCLUSIONS

The deep learning-based automatic brain segmentation and classification algorithm allowed an accurate diagnosis of Alzheimer disease using T1-weighted brain MR images. The widespread availability of T1-weighted brain MR imaging suggests that this algorithm is a promising and widely applicable method for predicting Alzheimer disease.

Quantitative evaluation of brain volume among elderly individuals in São Paulo, Brazil: a population-based study

Objective

To perform a quantitative analysis of the brain volume of elderly individuals in a population-based sample.

Materials and Methods

This was a radiological assessment and voxel-based quantitative analysis, with surface alignment, of 525 magnetic resonance imaging scans of individuals between 60 and 103 years of age who participated in the Saúde, Bem-estar e Envelhecimento (Health, Well-being, and Aging) study in the city of São Paulo, Brazil.

Results

We noted a median rate of reduction in total brain volume of 2.4% per decade after 60 years of age. Gray and white matter both showed volume reductions with age. The total brain volume/intracranial brain volume ratio differed between males and females.

Conclusion

We have corroborated the findings of studies conducted in the United States and Europe. The total brain volume/intracranial brain volume ratio is higher in men, representing a potential bias for the conventional radiological assessment of atrophy, which is typically based on the evaluation of the cerebrospinal fluid spaces.

Regional Cerebral Blood Flow in the Posterior Cingulate and Precuneus and the Entorhinal Cortical Atrophy Score Differentiate Mild Cognitive Impairment and Dementia Due to Alzheimer Disease

BACKGROUND AND PURPOSE

Alzheimer disease is the most common degenerative dementia affecting humans and mild cognitive impairment is considered the forerunner of this devastating illness with variable progression. Differentiating between them has become all the more essential with the advent of disease-modifying medications. The aim of this study was to test the utility of the entorhinal cortical atrophy score in combination with quantitative CBF in the posterior cingulate and precuneus using arterial spin-labeling to differentiate mild cognitive impairment and early Alzheimer disease.

MATERIALS AND METHODS

We analyzed MR imaging from a prospective data base of 3 age-matched groups: 21 cognitively healthy controls, 20 patients with mild cognitive impairment, and 19 patients with early Alzheimer disease. The highest entorhinal cortical atrophy score and an atlas-based measurement of CBF in the posterior cingulate and precuneus were estimated in these groups. Statistical comparison was performed among the groups for disease-prediction probability with these parameters independently and in combination using a binary logistic regression model.

RESULTS

The entorhinal cortical atrophy score performed well in distinguishing AD from HC, with a predicted probability of .887 (area under the curve, P < .001). The mean CBF of the posterior cingulate and precuneus was also found to be a useful discriminator (area under the curve, 0.810, P = < .001). Combining the entorhinal cortical atrophy score and CBF was the best predictor (area under the curve, 0.957, P < .001). In distinguishing mild cognitive impairment and Alzheimer disease, entorhinal cortical atrophy also did well with an area under the curve of 0.838 (P < .001). However regional CBF was not useful in differentiating them (area under the curve = 0.589, P = .339). Entorhinal cortical atrophy scored poorly in distinguishing mild cognitive impairment from healthy controls (AUC = 0.571, P = .493), but CBF fared well, with an area under the curve of 0.776 (P = .002).

CONCLUSIONS

Combining entorhinal cortical atrophy and regional CBF could be a potential imaging biomarker in distinguishing mild cognitive impairment and Alzheimer disease.