Insensitivity of visual assessment of hippocampal atrophy in familial Alzheimer's disease

Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals

Game theory-inspired deep learning using a generative adversarial network provides an environment to competitively interact and accomplish a goal. In the context of medical imaging, most work has focused on achieving single tasks such as improving image resolution, segmenting images, and correcting motion artifacts. We developed a dual-objective adversarial learning framework that simultaneously 1) reconstructs higher quality brain magnetic resonance images (MRIs) that 2) retain disease-specific imaging features critical for predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). We obtained 3-Tesla, T1-weighted brain MRIs of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=342) and the National Alzheimer's Coordinating Center (NACC, N = 190) datasets. We simulated MRIs with missing data by removing 50% of sagittal slices from the original scans (i.e., diced scans). The generator was trained to reconstruct brain MRIs using the diced scans as input. We introduced a classifier into the GAN architecture to discriminate between stable (i.e., sMCI) and progressive MCI (i.e., pMCI) based on the generated images to facilitate encoding of disease-related information during reconstruction. The framework was trained using ADNI data and externally validated on NACC data. In the NACC cohort, generated images had better image quality than the diced scans (Structural similarity (SSIM) index: 0.553 ± 0.116 versus 0.348 ± 0.108). Furthermore, a classifier utilizing the generated images distinguished pMCI from sMCI more accurately than with the diced scans (F1-score: 0.634 ± 0.019 versus 0.573 ± 0.028). Competitive deep learning has potential to facilitate disease-oriented image reconstruction in those at risk of developing Alzheimer's 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.

Familial cerebral amyloid disorders with prominent white matter involvement

Familial cerebral amyloid disorders are characterized by the accumulation of fibrillar protein aggregates, which deposit in the parenchyma as plaques and in the vasculature as cerebral amyloid angiopathy (CAA). Amyloid β (Aβ) is the most common of these amyloid proteins, accumulating in familial and sporadic forms of Alzheimer's disease and CAA. However, there are also a number of rare, hereditary, non-Aβ cerebral amyloidosis. The clinical manifestations of these familial cerebral amyloid disorders are diverse, including cognitive or neuropsychiatric presentations, intracerebral hemorrhage, seizures, myoclonus, headache, ataxia, and spasticity. Some mutations are associated with extensive white matter hyperintensities on imaging, which may or may not be accompanied by hemorrhagic imaging markers of CAA; others are associated with occipital calcification. We describe the clinical, imaging, and pathologic features of these disorders and discuss putative disease mechanisms. Familial disorders of cerebral amyloid accumulation offer unique insights into the contributions of vascular and parenchymal amyloid to pathogenesis and the pathways underlying white matter involvement in neurodegeneration. With Aβ immunotherapies now entering the clinical realm, gaining a deeper understanding of these processes and the relationships between genotype and phenotype has never been more relevant.

Clinical considerations in early-onset cerebral amyloid angiopathy

Cerebral amyloid angiopathy (CAA) is an important cerebral small vessel disease associated with brain haemorrhage and cognitive change. The commonest form, sporadic amyloid-β CAA, usually affects people in mid- to later life. However, early-onset forms, though uncommon, are increasingly recognized and may result from genetic or iatrogenic causes that warrant specific and focused investigation and management. In this review, we firstly describe the causes of early-onset CAA, including monogenic causes of amyloid-β CAA (APP missense mutations and copy number variants; mutations of PSEN1 and PSEN2) and non-amyloid-β CAA (associated with ITM2B, CST3, GSN, PRNP and TTR mutations), and other unusual sporadic and acquired causes including the newly-recognized iatrogenic subtype. We then provide a structured approach for investigating early-onset CAA, and highlight important management considerations. Improving awareness of these unusual forms of CAA amongst healthcare professionals is essential for facilitating their prompt diagnosis, and an understanding of their underlying pathophysiology may have implications for more common, late-onset, forms of the disease.

PSEN1 c.1292C<A Variant and Early-Onset Alzheimer’s Disease: A Scoping Review

Alzheimer’s disease (AD) is the most common cause of dementia, characterized by progressive loss of cognitive function, with β-amyloid plaques and neurofibrillary tangles being its major pathological findings. Although the disease mainly affects the elderly, c. 5–10% of the cases are due to PSEN1, PSEN2, and APP mutations, principally associated with an early onset of the disease. The A413E (rs63750083) PSEN1 variant, identified in 2001, is associated with early-onset Alzheimer’s disease (EOAD). Although there is scant knowledge about the disease’s clinical manifestations and particular features, significant clinical heterogeneity was reported, with a high incidence of spastic paraparesis (SP), language impairments, and psychiatric and motor manifestations. This scoping review aims to synthesize findings related to the A431E variant of PSEN1. In the search, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and the guidelines proposed by Arksey and O’Malley. We searched and identified 247 studies including the A431E variant of PSEN1 from 2001 to 2021 in five databases and one search engine. After the removal of duplicates, and apply inclusion criteria, 42 studies were finally included. We considered a narrative synthesis with a qualitative approach for the analysis of the data. Given the study sample conformation, we divided the results into those carried out only with participants carrying A431E (seven studies), subjects with PSEN variants (11 studies), and variants associated with EOAD in PSEN1, PSEN2, and APP (24 studies). The resulting synthesis indicates most studies involve Mexican and Mexican-American participants in preclinical stages. The articles analyzed included carrier characteristics in categories such as genetics, clinical, imaging techniques, neuropsychology, neuropathology, and biomarkers. Some studies also considered family members’ beliefs and caregivers’ experiences. Heterogeneity in both the studies found and carrier samples of EOAD-related gene variants does not allow for the generalization of the findings. Future research should focus on reporting data on the progression of carrier characteristics through time and reporting results independently or comparing them across variants.

Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data

The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD)" patients among a list of MCI patients. In this system we are using the power of longitudinal data extracted from magnetic resonance (MR). For this work, a total of 294 MCI patients were selected from the ADNI database. Among them, 125 patients developed AD during their follow-up and the rest remained stable. The proposed computer-aided diagnosis system (CAD) attempts to identify brain regions that are significant for the prediction of developing AD. The longitudinal data were constructed using a 3D Jacobian-based method aiming to track the brain differences between two consecutive follow-ups. The proposed CAD system distinguishes MCI patients who developed AD from those who remained stable with an accuracy of 87.2 percent. Moreover, it does not depend on data acquired by invasive methods or cognitive tests. This work demonstrates that the use of data in different time periods contains information that is beneficial for prognosis prediction purposes that outperform similar methods and are slightly inferior only to those systems that use invasive methods or neuropsychological tests.

The Association between HbA1c Levels, Olfactory Memory and Cognition in Normal, Pre-Diabetic and Diabetic Persons

BACKGROUND AND AIM

Recent data have shown that olfactory dysfunction is strongly related to Alzheimer's Disease (AD) that is often preceded by olfactory deficits suggesting that olfactory dysfunction might represent an early indicator of future cognitive in prediabetes.

METHODS

We have applied to a group of normal (n=15), prediabetic (n=16) and type 2 diabetic outpatients (n=15) olfactory testing, 1.5-T MRI scanner and detailed cognitive evaluation including the standard Mini-Mental State Examination (MMSE) form, Short Blessed Test (SBT), Letter Fluency Test (LFT) and the category fluency test with animal, Fruit and Vegetable Naming (CFT).

RESULTS

We have shown that Odour Threshold (OT), Discrimination (OD), and Identification (OI) scores and most cognitive test results were significantly different in the prediabetes and diabetes group compared to those in the control group. OD and OT were significantly different between the prediabetes and diabetes group, although the cognitive test results were only significantly different in the prediabetes and diabetes group compared to those in the control group. In evaluating the association between OI, OT, OD scores and specific cognitive tests, we have found, that impaired olfactory identification was the only parameter that correlated significantly with the SBT both in the pre-diabetes and diabetes group. Although spot glucose values were only correlated with OT, HbA1c levels were correlated with OT, OD, and OI, as well as results of the letter fluency test suggesting that HbA1c levels rather than the spot glucose values play a critical role in specific cognitive dysfunction.

CONCLUSION

To the best of our knowledge, this is the first prospective study to demonstrate a strong association between olfactory dysfunction and specific memory impairment in a population with prediabetes and diabetes suggesting that impaired olfactory identification might play an important role as a specific predictor of memory decline.

Memory Performance and Quantitative Neuroimaging Software in Mild Cognitive Impairment: A Concurrent Validity Study

OBJECTIVE

This study examined the relationship between patient performance on multiple memory measures and regional brain volumes using an FDA-cleared quantitative volumetric analysis program - Neuroreader™.

METHOD

Ninety-two patients diagnosed with mild cognitive impairment (MCI) by a clinical neuropsychologist completed cognitive evaluations and underwent MR Neuroreader™ within 1 year of testing. Select brain regions were correlated with three widely used memory tests. Regression analyses were conducted to determine if using more than one memory measures would better predict hippocampal z-scores and to explore the added value of recognition memory to prediction models.

RESULTS

Memory performances were most strongly correlated with hippocampal volumes than other brain regions. After controlling for encoding/Immediate Recall standard scores, statistically significant correlations emerged between Delayed Recall and hippocampal volumes (rs ranging from .348 to .490). Regression analysis revealed that evaluating memory performance across multiple memory measures is a better predictor of hippocampal volume than individual memory performances. Recognition memory did not add further predictive utility to regression analyses.

CONCLUSIONS

This study provides support for use of MR Neuroreader™ hippocampal volumes as a clinically informative biomarker associated with memory performance, which is a critical diagnostic feature of MCI phenotype.

APOE modifies the interaction of entorhinal cerebral blood flow and cortical thickness on memory function in cognitively normal older adults

OBJECTIVE

The ε4 allele of the apolipoprotein E (APOE) gene increases risk for cognitive decline in normal and pathologic aging. However, precisely how APOE ε4 exerts its negative impact on cognition is poorly understood. The present study aimed to determine whether APOE genotype (ε4+ vs. ε4-) modifies the interaction of medial temporal lobe (MTL) resting cerebral blood flow (CBF) and brain structure (cortical thickness [CT], volume [Vo]) on verbal memory performance.

METHODS

Multiple linear regression models were employed to investigate relationships between APOE genotype, arterial spin labeling MRI-measured CBF and FreeSurfer-based CT and Vo in four MTL regions of interest (left and right entorhinal cortex and hippocampus), and verbal memory performance among a sample of 117 cognitively normal older adults (41 ε4+, 76 ε4-) between the ages of 64 and 89 (mean age ​= ​73).

RESULTS

Results indicated that APOE genotype modified the interaction of CBF and CT on memory in the left entorhinal cortex, such that the relationship between entorhinal CBF and memory was negative (lower CBF was associated with better memory) in non-carriers with higher entorhinal CT, positive (higher CBF was associated with better memory) in non-carriers with lower entorhinal CT, and negative (higher CBF was associated with worse memory) in ε4 carriers with lower entorhinal CT.

CONCLUSIONS

Findings suggest that older adult APOE ε4 carriers may experience vascular dysregulation and concomitant morphological alterations in the MTL that interact to negatively affect memory even in the absence overt clinical symptoms, providing potential insight into the mechanistic link between APOE ε4 and detriments in cognition. Moreover, findings suggest a distinct multimodal neural signature in ε4 carriers (higher CBF and lower CT in the entorhinal cortex) that could aid in the identification of candidates for future clinical trials aimed at preventing or slowing cognitive decline. Differential findings with respect to ε4 carriers and non-carriers are discussed in the context of neurovascular compensation.

Genetic and degenerative disorders primarily causing dementia

Neuroimaging comprises a powerful set of instruments to diagnose the different causes of dementia, clarify their neurobiology, and monitor their treatment. Magnetic resonance imaging (MRI) depicts volume changes with neurodegeneration and inflammation, as well as abnormalities in functional and structural connectivity. MRI arterial spin labeling allows for the quantification of regional cerebral blood flow, characteristically altered in Alzheimer's disease, diffuse Lewy-body disease, and the frontotemporal dementias. Positron emission tomography allows for the determination of regional metabolism, with similar abnormalities as flow, and for the measurement of β-amyloid and abnormal tau deposition in the brain, as well as regional inflammation. These instruments allow for the quantification in vivo of most of the pathologic features observed in disorders causing dementia. Importantly, they allow for the longitudinal study of these abnormalities, having revealed, for instance, that the deposition of β-amyloid in the brain can antecede by decades the onset of dementia. Thus, a therapeutic window has been opened and the efficacy of immunotherapies directed at removing β-amyloid from the brain of asymptomatic individuals is currently being tested. Tau and inflammation imaging, still in their infancy, combined with genomics, should provide powerful insights into these disorders and facilitate their treatment.

Quantitative Neuroimaging Software for Clinical Assessment of Hippocampal Volumes on MR Imaging

BACKGROUND

Multiple neurological disorders including Alzheimer's disease (AD), mesial temporal sclerosis, and mild traumatic brain injury manifest with volume loss on brain MRI. Subtle volume loss is particularly seen early in AD. While prior research has demonstrated the value of this additional information from quantitative neuroimaging, very few applications have been approved for clinical use. Here we describe a US FDA cleared software program, NeuroreaderTM, for assessment of clinical hippocampal volume on brain MRI.

OBJECTIVE

To present the validation of hippocampal volumetrics on a clinical software program.

METHOD

Subjects were drawn (n = 99) from the Alzheimer Disease Neuroimaging Initiative study. Volumetric brain MR imaging was acquired in both 1.5 T (n = 59) and 3.0 T (n = 40) scanners in participants with manual hippocampal segmentation. Fully automated hippocampal segmentation and measurement was done using a multiple atlas approach. The Dice Similarity Coefficient (DSC) measured the level of spatial overlap between NeuroreaderTM and gold standard manual segmentation from 0 to 1 with 0 denoting no overlap and 1 representing complete agreement. DSC comparisons between 1.5 T and 3.0 T scanners were done using standard independent samples T-tests.

RESULTS

In the bilateral hippocampus, mean DSC was 0.87 with a range of 0.78-0.91 (right hippocampus) and 0.76-0.91 (left hippocampus). Automated segmentation agreement with manual segmentation was essentially equivalent at 1.5 T (DSC = 0.879) versus 3.0 T (DSC = 0.872).

CONCLUSION

This work provides a description and validation of a software program that can be applied in measuring hippocampal volume, a biomarker that is frequently abnormal in AD and other neurological disorders.

MTA index: a simple 2D-method for assessing atrophy of the medial temporal lobe using clinically available neuroimaging

Background and purpose: Despite a strong correlation to severity of AD pathology, the measurement of medial temporal lobe atrophy (MTA) is not being widely used in daily clinical practice as a criterion in the diagnosis of prodromal and probable AD. This is mainly because the methods available to date are sophisticated and difficult to implement for routine use in most hospitals—volumetric methods—or lack objectivity—visual rating scales. In this pilot study we aim to describe a new, simple and objective method for measuring the rate of MTA in relation to the global atrophy using clinically available neuroimaging and describe the rationale behind this method.

Description: This method consists of calculating a ratio with the area of 3 regions traced manually on one single coronal MRI slide at the level of the interpeduncular fossa: (1) the medial temporal lobe (MTL) region (A); (2) the parenchima within the medial temporal region, that includes the hippocampus and the parahippocampal gyrus—the fimbria taenia and plexus choroideus are excluded—(B); and (3) the body of the ipsilateral lateral ventricle (C). Therefrom we can compute the ratio “Medial Temporal Atrophy index” at both sides as follows: MTAi = (A − B)× 10/C.

Conclusions: The MTAi is a simple 2D-method for measuring the relative extent of atrophy in the MTL in relation to the global brain atrophy. This method can be useful for a more accurate diagnosis of AD in routine clinical practice. Further studies are needed to assess the usefulness of MTAi in the diagnosis of early AD, in tracking the progression of AD and in the differential diagnosis of AD with other dementias.

Genetic influences on atrophy patterns in familial Alzheimer's disease: a comparison of APP and PSEN1 mutations

Mutations in the presenilin1 (PSEN1) and amyloid β-protein precursor (APP) genes account for the majority of cases of autosomal dominantly inherited Alzheimer's disease (AD). We wished to assess and compare the patterns of cerebral loss produced by these two groups of mutations. Volumetric magnetic resonance imaging and neuropsychological assessments were performed in individuals with clinical AD carrying mutations in the APP (n = 10) and PSEN1 (n = 18) genes and in healthy controls (n = 18). Voxel-based morphometry (VBM), cortical thickness, and region of interest analyses were performed. Mini-Mental State Examination scores were similar in the two disease groups suggesting similar levels of disease severity. There was evidence that APP subjects have smaller hippocampal volume compared with PSEN1 subjects (p = 0.007), and weak evidence that they have larger whole-brain and grey matter volumes (both p = 0.07). Although there was no evidence of statistically significant differences between APP and PSEN1 in VBM or cortical thickness analyses, effect-maps were suggestive of APP subjects having more medial temporal lobe atrophy and conversely PSEN1 subjects showing more neocortical loss. Neuropsychological data were consistent with these regional differences and suggested greater memory deficits in the APP patients and greater impairment in non-memory domains in the PSEN1 group, although these differences were not statistically significant. We conclude that the mechanisms by which APP and PSEN1 mutations cause neuronal loss may differ which furthers our understanding of the neuropathology underlying AD and may inform future therapeutic strategies and trial designs.

The neurobiology of Alzheimer disease defined by neuroimaging

PURPOSE OF REVIEW

In 2011, a new set of new guidelines for the research diagnosis of three stages of Alzheimer disease was promulgated by the US National Institute of Aging and the Alzheimer Association. For the first time, they include the diagnosis of presymptomatic Alzheimer disease, recognizing that the disease process begins years before cognitive impairment develops. Awareness of this fact has largely been driven by neuroimaging, and particularly by imaging amyloid β (abeta) deposition in the brain, a procedure approved by the US Food and Drug Administration for clinical use in April 2012.

RECENT FINDINGS

In Alzheimer disease, abeta deposition antecedes, probably by decades, the onset of cognitive impairment. In brain regions with greatest abeta deposition, synaptic dysfunction can be imaged beginning at preclinical stages. In regions that are not identical with the ones with greatest abeta deposition but heavily connected with them, regional atrophy and loss of white-matter anisotropy can be detected later in the course of the disease, near the time when mild cognitive impairment supervenes. Together with neuropsychological testing, imaging can improve the prediction of worsening to Alzheimer disease among patients with mild cognitive impairment.

SUMMARY

These findings have huge implications for research on therapeutic approaches to Alzheimer disease. For instance, while so far only patients with the clinical diagnosis have been treated with immunotherapy targeting abeta removal, a consensus is building that to be effective, this therapy should be given in the preclinical stages of the disease, which are assessed most advantageously by means of neuroimaging.

Cortical and hippocampal atrophy in patients with autosomal dominant familial Alzheimer's disease

BACKGROUND

Both familial and sporadic Alzheimer's disease (AD) result in progressive cortical and subcortical atrophy. Familial autosomal dominant AD (FAD) allows us to study AD brain changes presymptomatically.

METHODS

33 subjects at risk for FAD (25 for PSEN1 and 8 for APP mutations; 22 mutation carriers and 11 controls) and 3 demented PSEN1 mutation carriers underwent T(1)-weighted MPRAGE 1.5T MRI. Using the hippocampal radial distance and cortical pattern matching techniques, we investigated the effects of carrier status and dementia diagnosis on cortical and hippocampal atrophy. All analyses were corrected for age and relative age (years to median age of disease onset in the family).

RESULTS

The dementia cases had pronounced cortical atrophy in the lateral and medial parietal, posterior cingulate and frontal cortices and hippocampal atrophy bilaterally relative to both nondemented carriers and controls. Nondemented carriers did not show significant cortical thinning or hippocampal atrophy relative to controls.

CONCLUSIONS

FAD is associated with thinning of the posterior association and frontal cortices and hippocampal atrophy. Larger sample sizes may be necessary to reliably identify cortical atrophy in presymptomatic carriers.