Risk estimation before progression to mild cognitive impairment and Alzheimer's disease: an AD resemblance atrophy index

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

The use of an anterior-posterior atrophy index to distinguish Alzheimer's disease from frontotemporal disorders: an automated volumetric MRI Study

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) require different treatments. Since clinical presentation can be nuanced, imaging biomarkers aid in diagnosis. Automated software such as Neuroreader (NR) provides volumetric imaging data, and indices between anterior and posterior brain areas have proven useful in distinguishing dementia subtypes in research cohorts. Existing indices are complex and require further validation in clinical settings.

PURPOSE

To provide initial validation for a simplified anterior-posterior index (API) from NR in distinguishing FTD and AD in a clinical cohort.

MATERIAL AND METHODS

A retrospective chart review was completed. We derived a simplified API: API = (logVA/VP-μ)/σ where V A is weighted volume of frontal and temporal lobes and V P of parietal and occipital lobes. μ and σ are the mean and standard deviation of logVA/VP computed for AD participants. Receiver operating characteristic (ROC) curves and regression analyses assessed the efficacy of the API versus brain areas in predicting diagnosis of AD versus FTD.

RESULTS

A total of 39 participants with FTD and 78 participants with AD were included. The API had an excellent performance in distinguishing AD from FTD with an area under the ROC curve of 0.82 and a positive association with diagnostic classification on logistic regression analysis (B = 1.491, P < 0.001).

CONCLUSION

The API successfully distinguished AD and FTD with excellent performance. The results provide preliminary validation of the API in a clinical setting.

Guideline for the cognitive assessment and follow-up in the Guangdong-Hong Kong-Macao Greater Bay Area (2024 edition)

This practice guideline focuses on the cognitive assessment for mild cognitive impairment in the Guangdong-Hong Kong-Macao Greater Bay Area. To achieve the standardization and normalization of its clinical practice and generate individualized intervention, the National Core Cognitive Center of the Second Affiliated Hospital of Guangzhou Medical University, the Cognitive Disorders Branch of Chinese Geriatic Society, the Dementia Group of Neurology Branch of Guangdong Medical Association and specialists from Hong Kong and Macao developed guidelines based on China's actual conditions and efficiency, economic cost and accuracy. The article addresses the significance, background, and the process of the assessment and follow-up to realize the promotion and dissemination of cognitive assessment.

Machine Learning-Derived MRI-Based Neurodegeneration Biomarker for Alzheimer's Disease: A Multi-Database Validation Study

BACKGROUND

Pilot study showed that Alzheimer's disease resemblance atrophy index (AD-RAI), a machine learning-derived MRI-based neurodegeneration biomarker of AD, achieved excellent diagnostic performance in diagnosing AD with moderate to severe dementia.

OBJECTIVE

The primary objective was to validate and compare the performance of AD-RAI with conventional volumetric hippocampal measures in diagnosing AD with mild dementia. The secondary objectives were 1) to investigate the association between imaging biomarkers with age and gender among cognitively unimpaired (CU) participants; 2) to analyze whether the performance of differentiating AD with mild dementia from CU will improve after adjustment for age/gender.

METHODS

AD with mild dementia (n = 218) and CU (n = 1,060) participants from 4 databases were included. We investigated the area under curve (AUC), sensitivity, specificity, and balanced accuracy of AD-RAI, hippocampal volume (HV), and hippocampal fraction (HF) in differentiating between AD and CU participants. Among amyloid-negative CU participants, we further analyzed correlation between the biomarkers with age/gender. We also investigated whether adjustment for age/gender will affect performance.

RESULTS

The AUC of AD-RAI (0.93) was significantly higher than that of HV (0.89) and HF (0.89). Subgroup analysis among A + AD and A- CU showed that AUC of AD-RAI (0.97) was also higher than HV (0.94) and HF (0.93). Diagnostic performance of AD-RAI and HF was not affected by age/gender while that of HV improved after age adjustment.

CONCLUSIONS

AD-RAI achieves excellent clinical validity and outperforms conventional volumetric hippocampal measures in aiding the diagnosis of AD mild dementia without the need for age adjustment.

Comparing machine learning-derived MRI-based and blood-based neurodegeneration biomarkers in predicting syndromal conversion in early AD

INTRODUCTION

We compared the machine learning-derived, MRI-based Alzheimer's disease (AD) resemblance atrophy index (AD-RAI) with plasma neurofilament light chain (NfL) level in predicting conversion of early AD among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects.

METHODS

We recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had the following data: clinical features (age, gender, education, Montreal Cognitive Assessment [MoCA]), structural MRI, plasma biomarkers (p-tau181 , NfL), cerebrospinal fluid biomarkers (CSF) (Aβ42, p-tau181 ), and apolipoprotein E (APOE) ε4 genotype. We defined AD using CSF Aβ42 (A+) and p-tau181 (T+). We defined conversion (C+) if a subject progressed to the next syndromal stage within 4 years.

RESULTS

Of 589 participants, 96 (16.3%) were A+T+C+. AD-RAI performed better than plasma NfL when added on top of clinical features, plasma p-tau181 , and APOE ε4 genotype (area under the curve [AUC] = 0.832 vs. AUC = 0.650 among CU, AUC = 0.853 vs. AUC = 0.805 among MCI) in predicting A+T+C+.

DISCUSSION

AD-RAI outperformed plasma NfL in predicting syndromal conversion of early AD.

HIGHLIGHTS

AD-RAI outperformed plasma NfL in predicting syndromal conversion among early AD. AD-RAI showed better metrics than volumetric hippocampal measures in predicting syndromal conversion. Combining clinical features, plasma p-tau181 and apolipoprotein E (APOE) with AD-RAI is the best model for predicting syndromal conversion.

Advancing Tau-PET quantification in Alzheimer's disease with machine learning: introducing THETA, a novel tau summary measure

Alzheimer's disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential.

Validation of the Alzheimer's disease-resemblance atrophy index in classifying and predicting progression in Alzheimer's disease

Background

Automated tools for characterising dementia risk have the potential to aid in the diagnosis, prognosis, and treatment of Alzheimer's disease (AD). Here, we examined a novel machine learning-based brain atrophy marker, the AD-resemblance atrophy index (AD-RAI), to assess its test-retest reliability and further validate its use in disease classification and prediction.

Methods

Age- and sex-matched 44 probable AD (Age: 69.13 ± 7.13; MMSE: 27-30) and 22 non-demented control (Age: 69.38 ± 7.21; MMSE: 27-30) participants were obtained from the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. Serial T1-weighted images (n = 678) from up to nine time points over a 2-year period, including 179 pairs of back-to-back scans acquired on same participants on the same day and 40 pairs of scans acquired at 2-week intervals were included. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and 2-week reproducibility were first assessed. The discriminative performance of AD-RAI was evaluated using the receiver operating characteristic curve, where DeLong's test was used to evaluate its performance against quantitative medial temporal lobe atrophy (QMTA) and hippocampal volume adjusted by intracranial volume (ICV)-proportions and ICV-residuals methods, respectively (HVR and HRV). Linear mixed-effects modelling was used to investigate longitudinal trajectories of AD-RAI and baseline AD-RAI prediction of cognitive decline. Finally, the longitudinal associations between AD-RAI and MMSE scores were assessed.

Results

AD-RAI had excellent same-day repeatability and excellent 2-week reproducibility. AD-RAI's AUC (99.8%; 95%CI = [99.3%, 100%]) was equivalent to that of QMTA (96.8%; 95%CI = [92.9%, 100%]), and better than that of HVR (86.8%; 95%CI = [78.2%, 95.4%]) or HRV (90.3%; 95%CI = [83.0%, 97.6%]). While baseline AD-RAI was significantly higher in the AD group, it did not show detectable changes over 2 years. Baseline AD-RAI was negatively associated with MMSE scores and the rate of the change in MMSE scores over time. A negative longitudinal association was also found between AD-RAI values and the MMSE scores among AD patients.

Conclusions

The AD-RAI represents a potential biomarker that may support AD diagnosis and be used to predict the rate of future cognitive decline in AD patients.

Automated brain volumetric measures with AccuBrain: version comparison in accuracy, reproducibility and application for diagnosis

Background

Automated brain volumetry has been widely used to assess brain volumetric changes that may indicate clinical states and progression. Among the tools that implement automated brain volumetry, AccuBrain has been validated for its accuracy, reliability and clinical applications for the older version (IV1.2). Here, we aim to investigate the performance of an updated version (IV2.0) of AccuBrain for future use from several aspects.

Methods

Public datasets with 3D T1-weighted scans were included for version comparisons, each with Alzheimer’s disease (AD) patients and normal control (NC) subjects that were matched in age and gender. For the comparisons of the brain volumetric measures quantified from the same scans, we investigated the difference of hippocampal segmentation accuracy (using Dice similarity coefficient [DSC] as the major measurement). As AccuBrain generates a composite index (AD resemblance atrophy index, AD-RAI) that indicates similarity with AD-like brain atrophy pattern, we also compared the two versions for the diagnostic accuracy of AD versus NC with AD-RAI. Also, we examined the intra-scanner reproducibility of the two versions for the scans acquired with short-intervals using intraclass correlation coefficient.

Results

AccuBrain IV2.0 presented significantly higher accuracy of hippocampal segmentation (DSC: 0.91 vs. 0.89, p < 0.001) and diagnostic accuracy of AD (AUC: 0.977 vs. 0.921, p < 0.001) than IV1.2. The results of intra-scanner reproducibility did not favor one version over the other.

Conclusions

AccuBrain IV2.0 presented better segmentation accuracy and diagnostic accuracy of AD, and similar intra-scanner reproducibility compared with IV1.2. Both versions should be feasible for use due to the small magnitude of differences.

Cholesterol efflux capacity of HDL was not associated with cognitive decline and brain structures in older people with diabetes mellitus

Aims/Introduction

To examine the association between cholesterol efflux capacity (CEC) of serum high‐density lipoprotein (HDL) and cognitive function and brain structures in older people with diabetes mellitus.

Materials and Methods

Participants of a randomized placebo‐controlled trial of 27‐month vitamin B₁₂ supplementation in older people with diabetes mellitus, which showed no effect on cognition, were further followed up at month 72. Cognitive tests included the Clinical Dementia Rating scale, Neuropsychological Test Battery in memory, executive function and psychomotor speed. Brain magnetic resonance imaging scans were carried out in a subset at baseline, month 27 and month 45. Fasting serum at baseline, month 9, month 27 and month 72 were analyzed for adenosine triphosphate‐binding cassette transporter A1‐mediated CEC of HDL and apolipoprotein A1 (ApoA1).

Results

Serum HDL cholesterol at baseline was associated with better executive and memory function at follow up. Serum ApoA1 was associated with a better memory Z‐score at month 18. Serum CEC and ApoA1 were not associated with Clinical Dementia Rating scale, Neuropsychological Test Battery, hippocampal volume and white matter disease on magnetic resonance imaging at baseline and whole brain atrophy rates. They were also not associated with cognitive function at month 27 and 72 on multilevel modeling. CEC and ApoA1 decreased significantly from baseline to month 27. Faster decliners in CEC had a greater increase in brain peak width of skeletonized mean diffusivity.

Conclusions

Higher serum HDL cholesterol was associated with more favorable changes in memory and executive function in older people with diabetes mellitus. However, this was not due to CEC or ApoA1. A decline in CEC was associated with small vessel disease in the brain.

The Correlation Between White Matter Hyperintensity Burden and Regional Brain Volumetry in Patients With Alzheimer's Disease

Background

White matter hyperintensities (WMHs) and regional brain lobe atrophy coexist in the brain of patients with Alzheimer's disease (AD), but the association between them in patients with AD still lacks comprehensive investigation and solid imaging data support.

Objective

We explored whether WMHs can promote the pathological process of AD by aggravating atrophy in specific brain regions and tried to explain the regional specificity of these relationships.

Methods

A sample of 240 adults including 180 normal controls (NCs) and 80 cases with AD were drawn from the ADNI database. T1-weighted magnetic resonance imaging (MRI) and T2-weighted fluid-attenuated MRI of the participants were downloaded and were analyzed using AccuBrain^® to generate the quantitative ratio of WMHs (WMHr, WMH volumes corrected by intracranial volume) and regional brain atrophy. We also divided WMHr into periventricular WMHr (PVWMHr) and deep WMHr (DWMHr) for the purpose of this study. The Cholinergic Pathways Hyperintensities Scale (CHIPS) scores were conducted by two evaluators. Independent t-test, Mann–Whitney U test, or χ² test were used to compare the demographic characteristics, and Spearman correlation coefficient values were used to determine the association between WMHs and different regions of brain atrophy.

Results

Positive association between WMHr and quantitative medial temporal lobe atrophy (QMTA) (r_s = 0.281, p = 0.011), temporal lobe atrophy (r_s = 0.285, p = 0.011), and insular atrophy (r_s = 0.406, p < 0.001) was found in the AD group before Bonferroni correction. PVWMHr contributed to these correlations. By separately analyzing the relationship between PVWMHr and brain atrophy, we found that there were still positive correlations after correction in QMTA (r_s = 0.325, p = 0.003), temporal lobe atrophy (r_s = 0.298, p = 0.007), and insular atrophy (r_s = 0.429, p < 0.001) in AD group.

Conclusion

WMH severity tends to be associated with regional brain atrophy in patients with AD, especially with medial temporal lobe, temporal lobe, and insular lobe atrophy. PVWMHs were devoted to these correlations.

Brain MRI Biomarkers to Predict Cognitive Decline in Older People with Alzheimer's Disease

BACKGROUND

Structural magnetic resonance imaging markers predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD). However, the correlation between baseline MRI findings and AD progression has not been fully established.

OBJECTIVE

To explore the correlation between baseline MRI findings and AD progression.

METHODS

Brain volumetric measures were applied to differentiate the patients at risk of fast deterioration in AD. We included 194 AD patients with a 24-month follow-up: 65 slow decliners, 63 normal decliners, and 66 fast decliners categorized by changes in Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog). ANOVA analyses were used to identify baseline brain atrophy between groups. Logistic regressions were further performed to explore the relative merits of AD resemblance structural atrophy index (AD-RAI) and individual regional volumetric measures in prediction of disease progression.

RESULTS

Atrophy in the temporal and insular lobes was associated with fast cognitive decline over 24 months. Smaller volumes of temporal and insular lobes in the left but not the right brain were associated with fast cognitive decline. Baseline AD-RAI predicted fast versus slow progression of cognitive decline (odds ratio 3.025 (95% CI: 1.064-8.600), high versus low, AUC 0.771). Moreover, AD-RAI was significantly lower among slow decliners when compared with normal decliners (p = 0.039).

CONCLUSION

AD-RAI on MRI showed potential in identifying clinical AD patients at risk of accelerated cognitive decline.

AD Resemblance Atrophy Index of Brain Magnetic Resonance Imaging in Predicting the Progression of Mild Cognitive Impairment Carrying Apolipoprotein E-ε4 Allele

Background and Objective

Early identification is important for timely Alzheimer’s disease (AD) treatment. Apolipoprotein E ε4 allele (APOE-ε4) is an important genetic risk factor for sporadic AD. The AD-Resemblance Atrophy Index (RAI)—a structural magnetic resonance imaging-derived composite index—was found to predict the risk of progression from mild cognitive impairment (MCI) to AD. Therefore, we investigated whether the AD-RAI can predict cognitive decline and progression to AD in patients with MCI carrying APOE ε4.

Methods

We included 733 participants with MCI from the Alzheimer’s Disease Neuroimaging Initiative Database (ADNI). Their APOE genotypes, cognitive performance, and levels of AD-RAI were assessed at baseline and follow-up. Linear regression models were used to test the correlations between the AD-RAI and baseline cognitive measures, and linear mixed models with random intercepts and slopes were applied to investigate whether AD-RAI and APOE-ε4 can predict the level of cognitive decline. Cox proportional risk regression models were used to test the association of AD-RAI and APOE status with the progression from MCI to AD.

Results

The baseline AD-RAI was higher in the MCI converted to AD group than in the MCI stable group (P < 0.001). The AD-RAI was significantly correlated with cognition, and had a synergistic effect with APOE-ε4 to predict the rate of cognitive decline. The AD-RAI predicted the risk and timing of MCI progression to AD. Based on the MCI population carrying APOE-ε4, the median time to progression from MCI to AD was 24 months if the AD-RAI > 0.5, while the median time to progression from MCI to AD was 96 months for patients with an AD-RAI ≤ 0.5.

Conclusion

The AD-RAI can predict the risk of progression to AD in people with MCI carrying APOE ε4, is strongly correlated with cognition, and can predict cognitive decline.

MRI Volumetric Analysis of the Thalamus and Hypothalamus in Amyotrophic Lateral Sclerosis

Background: Increasing evidence has shown that amyotrophic lateral sclerosis (ALS) can result in abnormal energy metabolism and sleep disorders, even before motor dysfunction. Although the hypothalamus and thalamus are important structures in these processes, few ALS studies have reported abnormal MRI structural findings in the hypothalamus and thalamus.

Purpose: We aimed to investigate volumetric changes in the thalamus and hypothalamus by using the automatic brain structure volumetry tool AccuBrain^®.

Methods: 3D T1-weighted magnetization-prepared gradient echo imaging (MPRAGE) scans were acquired from 16 patients with ALS with normal cognitive scores and 16 age-, sex- and education-matched healthy controls. Brain tissue and structure volumes were automatically calculated using AccuBrain^®.

Results: There were no significant differences in bilateral thalamic (F = 1.31, p = 0.287) or hypothalamic volumes (F = 1.65, p = 0.213) between the ALS and control groups by multivariate analysis of covariance (MANCOVA). Left and right hypothalamic volumes were correlated with whole-brain volume in patients with ALS (t = 3.19, p = 0.036; t = 3.03, p = 0.044), while the correlation between age and bilateral thalamic volumes tended to be significant after Bonferroni correction (t = 2.76, p = 0.068; t = 2.83, p = 0.06). In the control group, left and right thalamic volumes were correlated with whole-brain volume (t = 4.26, p = 0.004; t = 4.52, p = 0.004).

Conclusion: Thalamic and hypothalamic volumes did not show differences between patients with normal frontotemporal function ALS and healthy controls, but further studies are still needed.

Alzheimer resemblance atrophy index, BrainAGE, and normal pressure hydrocephalus score in the prediction of subtle cognitive decline: added value compared to existing MR imaging markers

OBJECTIVES

Established visual brain MRI markers for dementia include hippocampal atrophy (mesio-temporal atrophy MTA), white matter lesions (Fazekas score), and number of cerebral microbleeds (CMBs). We assessed whether novel quantitative, artificial intelligence (AI)-based volumetric scores provide additional value in predicting subsequent cognitive decline in elderly controls.

METHODS

A prospective study including 80 individuals (46 females, mean age 73.4 ± 3.5 years). 3T MR imaging was performed at baseline. Extensive neuropsychological assessment was performed at baseline and at 4.5-year follow-up. AI-based volumetric scores were derived from 3DT1: Alzheimer Disease Resemblance Atrophy Index (AD-RAI), Brain Age Gap Estimate (BrainAGE), and normal pressure hydrocephalus (NPH) index. Analyses included regression models between cognitive scores and imaging markers.

RESULTS

AD-RAI score at baseline was associated with Corsi (visuospatial memory) decline (10.6% of cognitive variability in multiple regression models). After inclusion of MTA, CMB, and Fazekas scores simultaneously, the AD-RAI score remained as the sole valid predictor of the cognitive outcome explaining 16.7% of its variability. Its percentage reached 21.4% when amyloid positivity was considered an additional explanatory factor. BrainAGE score was associated with Trail Making B (executive functions) decrease (8.5% of cognitive variability). Among the conventional MRI markers, only the Fazekas score at baseline was positively related to the cognitive outcome (8.7% of cognitive variability). The addition of the BrainAGE score as an independent variable significantly increased the percentage of cognitive variability explained by the regression model (from 8.7 to 14%). The addition of amyloid positivity led to a further increase in this percentage reaching 21.8%.

CONCLUSIONS

The AI-based AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.

KEY POINTS

• AD-RAI score at baseline was associated with Corsi score (visuospatial memory) decline. • BrainAGE score was associated with Trail Making B (executive functions) decrease. • AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.

Brain Volumetric Alterations in Preclinical HIV-Associated Neurocognitive Disorder Using Automatic Brain Quantification and Segmentation Tool

Purpose

This study aimed to determine if people living with HIV (PLWH) in preclinical human immunodeficiency virus (HIV)-associated neurocognitive disorder (HAND), with no clinical symptoms and without decreased daily functioning, suffer from brain volumetric alterations and its patterns.

Method

Fifty-nine male PLWH at the HAND preclinical stage were evaluated, including 19 subjects with asymptomatic neurocognitive impairment (ANI), 17 subjects with cognitive abnormality that does not reach ANI (Not reach ANI), and 23 subjects with cognitive integrity. Moreover, 23 healthy volunteers were set as the seronegative normal controls (NCs). These individuals underwent sagittal three-dimensional T₁-weighted imaging (3D T₁WI). Quantified data and volumetric measures of brain structures were automatically segmented and extracted using AccuBrain^®. In addition, the multiple linear regression analysis was performed to analyze the relationship of volumes of brain structures and clinical variables in preclinical HAND, and the correlations of the brain volume parameters with different cognitive function states were assessed by Pearson's correlation analysis.

Results

The significant difference was shown in the relative volumes of the ventricular system, bilateral lateral ventricle, thalamus, caudate, and left parietal lobe gray matter between the preclinical HAND and NCs. Furthermore, the relative volumes of the bilateral thalamus in preclinical HAND were negatively correlated with attention/working memory (left: r = -0.271, p = 0.042; right: r = -0.273, p = 0.040). Higher age was associated with increased relative volumes of the bilateral lateral ventricle and ventricular system and reduced relative volumes of the left thalamus and parietal lobe gray matter. The lower CD4⁺/CD8⁺ ratio was associated with increased relative volumes of the left lateral ventricle and ventricular system. Longer disease course was associated with increased relative volumes of the bilateral thalamus. No significant difference was found among preclinical HAND subgroups in all indices, and the difference between the individual groups (Not reach ANI and Cognitive integrity groups) and NCs was also insignificant. However, there was a significant difference between ANI and NCs in the relative volumes of the bilateral caudate and lateral ventricle.

Conclusion

Male PLWH at the HAND preclinical stage suffer from brain volumetric alterations. AccuBrain^® provides potential value in evaluating HIV-related neurocognitive dysfunction.

Advances in computerized MRI‐based biomarkers in Alzheimer’s disease

The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.

MRI-based Alzheimer's disease-resemblance atrophy index in the detection of preclinical and prodromal Alzheimer's disease

Alzheimer's Disease-resemblance atrophy index (AD-RAI) is an MRI-based machine learning derived biomarker that was developed to reflect the characteristic brain atrophy associated with AD. Recent study showed that AD-RAI (≥0.5) had the best performance in predicting conversion from mild cognitive impairment (MCI) to dementia and from cognitively unimpaired (CU) to MCI. We aimed to validate the performance of AD-RAI in detecting preclinical and prodromal AD. We recruited 128 subjects (MCI=50, CU=78) from two cohorts: CU-SEEDS and ADNI. Amyloid (A+) and tau (T+) status were confirmed by PET (¹¹C-PIB, ¹⁸F-T807) or CSF analysis. We investigated the performance of AD-RAI in detecting preclinical and prodromal AD (i.e. A+T+) among MCI and CU subjects and compared its performance with that of hippocampal measures. AD-RAI achieved the best metrics among all subjects (sensitivity 0.74, specificity 0.91, accuracy 85.94%) and among MCI subjects (sensitivity 0.92, specificity 0.81, accuracy 86.00%) in detecting A+T+ subjects over other measures. Among CU subjects, AD-RAI yielded the best specificity (0.95) and accuracy (85.90%) over other measures, while hippocampal volume achieved a higher sensitivity (0.73) than AD-RAI (0.47) in detecting preclinical AD. These results showed the potential of AD-RAI in the detection of early AD, in particular at the prodromal stage.

AD Resemblance Atrophy Index as a Diagnostic Biomarker for Alzheimer's Disease: A Retrospective Clinical and Biological Validation

BACKGROUND

Magnetic resonance imaging (MRI) provides objective information about brain structural atrophy in patients with Alzheimer's disease (AD). This multi-structural atrophic information, when integrated as a single differential index, has the potential to further elevate the accuracy of AD identification from normal control (NC) compared to the conventional structure volumetric index.

OBJECTIVE

We herein investigated the performance of such an MRI-derived AD index, AD-Resemblance Atrophy Index (AD-RAI), as a neuroimaging biomarker in clinical scenario.

METHOD

Fifty AD patients (19 with the Amyloid, Tau, Neurodegeneration (ATN) results assessed in cerebrospinal fluid) and 50 age- and gender-matched NC (19 with ATN results assessed using positron emission tomography) were recruited in this study. MRI-based imaging biomarkers, i.e., AD-RAI, were quantified using AccuBrain®. The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of these MRI-based imaging biomarkers were evaluated with the diagnosis result according to clinical criteria for all subjects and ATN biological markers for the subgroup.

RESULTS

In the whole groups of AD and NC subjects, the accuracy of AD-RAI was 91%, sensitivity and specificity were 88% and 96%, respectively, and the AUC was 92%. In the subgroup of 19 AD and 19 NC with ATN results, AD-RAI results matched completely with ATN classification. AD-RAI outperforms the volume of any single brain structure measured.

CONCLUSION

The finding supports the hypothesis that MRI-derived composite AD-RAI is a more accurate imaging biomarker than individual brain structure volumetry in the identification of AD from NC in the clinical scenario.

An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer's disease

BACKGROUND

The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer's disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD.

METHODS

In this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in "FTD dominant" structures over that obtained from "AD dominant" structures. The weights and the identification of "FTD/AD dominant" structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases.

RESULTS

AD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p < 0.001 and AUC = 0.88) as we reported before, while no significant difference was found between AD and FTD (p = 0.647). FTDI showed excellent accuracy in identifying FTD from AD (AUC = 0.90; SEN = 89%, SPE = 75% with threshold value = 1.08). The validation using independent data from ADNI and FTLDNI datasets also confirmed the efficacy of FTDI (AUC = 0.93; SEN = 96%, SPE = 70% with threshold value = 1.08).

CONCLUSIONS

Brain atrophy in AD, FTD, and normal elderly shows distinct patterns. In addition to AD-RAI that is designed to detect abnormal brain atrophy in dementia, a novel index specific to FTD is proposed and validated. By combining AD-RAI and FTDI, an MRI-based decision strategy was further proposed as a promising solution for the differential diagnosis of AD and FTD in clinical practice.

Brain volume and perfusion asymmetry in temporal lobe epilepsy with and without hippocampal sclerosis

Objectives: To detect and compare the features of interictal perfusion and volume asymmetry between temporal lobe epilepsy (TLE) patients with and without hippocampal sclerosis (HS).Methods: Sixty-one TLE patients (mean age 28.4 ± 9.3 years; 28 female/33 male) with unilateral signs of HS (TLE-HS+) and 25 TLE patients (mean age 29.8 ± 8.0 years; 17 female/8 male) without HS (TLE-HS-) were included. Thirty healthy volunteers served as controls (mean age 26.0 ± 8.7 years; 22 female/8 male). Brain segmentation and volume calculation were performed. Quantitative cerebral blood flow (CBF) values were measured based on arterial spin labeling (ASL). The asymmetry indices (AIs) of volume and perfusion were calculated.Results: TLE-HS+ (adjusted P = 0.001) and TLE-HS- patients (adjusted P = 0.006) had significantly higher hippocampal perfusion AIs than controls. TLE-HS+ and TLE-HS- had similar hippocampal perfusion AIs (adjusted P = 1.00). TLE-HS+ had higher hippocampal volume AIs than TLE-HS- and controls (adjusted P < 0.001). TLE-HS- and controls had similar hippocampal volume AIs (adjusted P = 1.00). All (100%) TLE-HS+ patients had positive hippocampal perfusion or volume AIs. No significant correlation between the AIs of hippocampal perfusion and volume was found in both TLE-HS+(P = 0.894) and TLE-HS- (P = 0.106) patients. TLE-HS+ patients demonstrated more extensive whole-brain asymmetry of both perfusion and volume than TLE-HS- patients.Conclusion: TLE-HS+ and TLE-HS- patients have different patterns of whole-brain perfusion and volume asymmetry. Hippocampal perfusion asymmetry was revealed in both TLE-HS+ and TLE-HS- patients.

Structural changes in the lobar regions of brain in cerebral small-vessel disease patients with and without cognitive impairment: An MRI-based study with automated brain volumetry

PURPOSE

This study aims to investigate the alterations of brain volumetry and associated structural covariance at lobar level in cerebral small-vessel disease (CSVD) with and without cognitive impairment.

METHOD

Twenty-seven CSVD patients with mild cognitive impairment (CSVD-MCI), 37 CSVD patients with normal cognition (CSVD-NC), and 35 controls, underwent T1-weighted imaging of magnetic resonance. Volume of gray matter (GM) and white matter (WM) and a lobar atrophy index that measures the ratio of cerebrospinal fluid to brain parenchyma were quantified for each lobe. One-way ANOVA with multiple comparison corrections was performed to compare these volumetric measures. Volumetric structural covariance analyses were performed with lobar atrophy indexes to investigate the alterations of anatomical covariance within each pair of lobar regions in CSVD-NC and CSVD-MCI subjects compared with controls.

RESULTS

CSVD-NC subjects presented no significant volumetric differences with controls in any of the lobar regions. Compared with controls, CSVD-MCI patients presented significantly smaller volume of GM in bilateral frontal and parietal lobes, significantly smaller volume of WM in right cingulate lobe, and significantly larger lobar atrophy indexes of bilateral temporal, insular lobes and left cingulate lobe (P < 0.05). Both CSVD-NC group and CSVD-MCI group showed significant differences of structural covariance as measured by lobar atrophy index compared with controls. In particular, CSVD-MCI group showed even more extensive alterations of structural covariance, especially in bilateral cingulate and temporal lobes.

CONCLUSIONS

There are alterations of brain volumetry and associated structural covariance within lobar regions in CSVD, which indicates the potential brain structural reorganization in CSVD.