Antemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND

Visual dysfunction in dementia with Lewy bodies

PURPOSE OF REVIEW

To review the literature on visual dysfunction in dementia with Lewy bodies (DLB), including its mechanisms and clinical implications.

RECENT FINDINGS

Recent studies have explored novel aspects of visual dysfunction in DLB, including visual texture agnosia, mental rotation of 3-dimensional drawn objects, and reading fragmented letters. Recent studies have shown parietal and occipital hypoperfusion correlating with impaired visuoconstruction performance. While visual dysfunction in clinically manifest DLB is well recognized, recent work has focused on prodromal or mild cognitive impairment (MCI) due to Lewy body pathology with mixed results. Advances in retinal imaging have recently led to the identification of abnormalities such as parafoveal thinning in DLB. Patients with DLB experience impairment in color perception, form and object identification, space and motion perception, visuoconstruction tasks, and illusions in association with visual cortex and network dysfunction. These symptoms are associated with visual hallucinations, driving impairment, falls, and other negative outcomes.

AI-based differential diagnosis of dementia etiologies on multimodal data

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

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.

AI-based differential diagnosis of dementia etiologies on multimodal data

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.

Increased Likelihood of Dementia with Coexisting Atrophy of Multiple Regions of Interest

BACKGROUND

Brain volume is associated with cognitive decline in later life, and cortical brain atrophy exceeding the normal range is related to inferior cognitive and behavioral outcomes in later life.

OBJECTIVE

To investigate the likelihood of cognitive decline, mild cognitive impairment (MCI), or dementia, when regional atrophy is present in participants' magnetic resonance imaging (MRI).

METHODS

Multi-center MRI data of 2,545 adults were utilized to measure regional volumes using NEUROPHET AQUA. Four lobes (frontal, parietal, temporal, and occipital), four Alzheimer's disease-related regions (entorhinal, fusiform, inferior temporal, and middle temporal area), and the hippocampus in the left and right hemispheres were measured and analyzed. The presence of regional atrophy from brain MRI was defined as ≤1.5 standard deviation (SD) compared to the age- and sex-matched cognitively normal population. The risk ratio for cognitive decline was investigated for participants with regional atrophy in contrast to those without regional atrophy.

RESULTS

The risk ratio for cognitive decline was significantly higher when hippocampal atrophy was present (MCI, 1.84, p < 0.001; dementia, 4.17, p < 0.001). Additionally, participants with joint atrophy in multiple regions showed a higher risk ratio for dementia, e.g., 9.6 risk ratio (95% confidence interval, 8.0-11.5), with atrophy identified in the frontal, temporal, and hippocampal gray matter, than those without atrophy.

CONCLUSIONS

Our study showed that individuals with multiple regional atrophy (either lobar or AD-specific regions) have a higher likelihood of developing dementia compared to the age- and sex-matched population without atrophy. Thus, further consideration is needed when assessing MRI findings.

The use of neuroimaging techniques in the early and differential diagnosis of dementia

Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer's disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.

Study of Alzheimer's disease- and frontotemporal dementia-associated genes in the Cretan Aging Cohort

Using exome sequencing, we analyzed 196 participants of the Cretan Aging Cohort (CAC; 95 with Alzheimer's disease [AD], 20 with mild cognitive impairment [MCI], and 81 cognitively normal controls). The APOE ε4 allele was more common in AD patients (23.2%) than in controls (7.4%; p < 0.01) and the PSEN2 p.Arg29His and p.Cys391Arg variants were found in 3 AD and 1 MCI patient, respectively. Also, we found the frontotemporal dementia (FTD)-associated TARDBP gene p.Ile383Val variant in 2 elderly patients diagnosed with AD and in 2 patients, non CAC members, with the amyotrophic lateral sclerosis/FTD phenotype. Furthermore, the p.Ser498Ala variant in the positively selected GLUD2 gene was less frequent in AD patients (2.11%) than in controls (16%; p < 0.01), suggesting a possible protective effect. While the same trend was found in another local replication cohort (n = 406) and in section of the ADNI cohort (n = 808), this finding did not reach statistical significance and therefore it should be considered preliminary. Our results attest to the value of genetic testing to study aged adults with AD phenotype.

Early Diagnosis of Brain Diseases Using Artificial Intelligence and EV Molecular Data: A Proposed Noninvasive Repeated Diagnosis Approach

Brain-derived extracellular vesicles (BDEVs) are released from the central nervous system. Brain-related research and diagnostic techniques involving BDEVs have rapidly emerged as a means of diagnosing brain disorders because they are minimally invasive and enable repeatable measurements based on body fluids. However, EVs from various cells and organs are mixed in the blood, acting as potential obstacles for brain diagnostic systems using BDEVs. Therefore, it is important to screen appropriate brain EV markers to isolate BDEVs in blood. Here, we established a strategy for screening potential BDEV biomarkers. To collect various molecular data from the BDEVs, we propose that the sensitivity and specificity of the diagnostic system could be enhanced using machine learning and AI analysis. This BDEV-based diagnostic strategy could be used to diagnose various brain diseases and will help prevent disease through early diagnosis and early treatment.

Diverse limbic comorbidities cause limbic and temporal atrophy in lewy body disease

BACKGROUND

In contrast to Alzheimer's disease (AD)-related pathology, the influence of comorbid limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) or argyrophilic grains (AG) on structural imaging in Lewy body disease (LBD) has seldom been evaluated.

OBJECTIVE

This study aimed to investigate whether non-AD limbic comorbidities, including LATE-NC and AG, cause cortical atrophy in LBD.

METHODS

Seventeen patients with pathologically confirmed LBD with lower Braak neurofibrillary tangle stage (<IV) and 10 healthy controls (HC) were included. Based on the presence of comorbid LATE-NC or AG, LBD patients were subdivided into nine patients with these proteinopathies (mixed LBD [mLBD]) and eight without (pure LBD [pLBD]). In addition to clinical feature evaluation, gray matter atrophy on voxel-based morphometry was compared between the two LBD and HC groups.

RESULTS

The mean age at antemortem magnetic resonance imaging of the mLBD patients was higher than that of the pLBD patients (84.3 ± 3.9 vs. 76.5 ± 10.5; p = .046). Irrespective of the presence or absence of comorbid LATE-NC or AG, all patients were clinically diagnosed with probable dementia with Lewy bodies or Parkinson's disease with dementia, respectively. Compared to the pLBD group, the mLBD group showed more conspicuous cortical atrophy of the bilateral hippocampus, amygdala, and temporal pole.

CONCLUSIONS

Non-AD limbic comorbidities, including LATE-NC and AG, are associated with limbic and temporal atrophy in older patients with LBD. Therefore, the possibility of non-AD limbic comorbidities should be considered in the diagnosis of elderly patients with dementia with clinical symptoms of LBD and medial temporal atrophy.

Functional Imaging for Neurodegenerative Diseases

Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.

Fractal dimension of the brain in neurodegenerative disease and dementia: A systematic review

Sensitive and specific antemortem biomarkers of neurodegenerative disease and dementia are crucial to the pursuit of effective treatments, required both to reliably identify disease and to track its progression. Atrophy is the structural magnetic resonance imaging (MRI) hallmark of neurodegeneration. However in most cases it likely indicates a relatively advanced stage of disease less susceptible to treatment as some disease processes begin decades prior to clinical onset. Among emerging metrics that characterise brain shape rather than volume, fractal dimension (FD) quantifies shape complexity. FD has been applied in diverse fields of science to measure subtle changes in elaborate structures. We review its application thus far to structural MRI of the brain in neurodegenerative disease and dementia. We identified studies involving subjects who met criteria for mild cognitive impairment, Alzheimer's Disease, Vascular Dementia, Lewy Body Dementia, Frontotemporal Dementia, Amyotrophic Lateral Sclerosis, Parkinson's Disease, Huntington's Disease, Multiple Systems Atrophy, Spinocerebellar Ataxia and Multiple Sclerosis. The early literature suggests that neurodegenerative disease processes are usually associated with a decline in FD of the brain. The literature includes examples of disease-related change in FD occurring independently of atrophy, which if substantiated would represent a valuable advantage over other structural imaging metrics. However, it is likely to be non-specific and to exhibit complex spatial and temporal patterns. A more harmonious methodological approach across a larger number of studies as well as careful attention to technical factors associated with image processing and FD measurement will help to better elucidate the metric's utility.

The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation

INTRODUCTION

While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterised based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely on complex computational models and large training datasets to categorise individual patients into diagnostic subgroups based on distinguishing imaging features. Reliable individual subject data interpretation is hugely important in the clinical setting to expedite the diagnosis and classify individuals into relevant prognostic categories.

AREAS COVERED

This article reviews (1) the neuroimaging studies that propose single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) potential practical implications and (3) the limitations of current single-subject data interpretation models.

EXPERT OPINION

Classification studies in FTLD have demonstrated the feasibility of categorising individual subjects into diagnostic groups based on multiparametric imaging data. Preliminary data indicate that pre-symptomatic FTLD mutation carriers may also be reliably distinguished from controls. Despite momentous advances in the field, significant further improvements are needed before these models can be developed into viable clinical applications.

Dynamic Aphasia as a Variant of Frontotemporal Dementia

We describe two individuals with progressive verbal difficulty who exhibited impairment of propositional language, with relatively well-preserved auditory comprehension, naming, and repetition-a profile that is consistent with dynamic aphasia. By providing a brief review of pertinent literature and the results from our neurologic, speech and language, neuropsychological, and neuroimaging testing, this report sheds light on the infrequently reported dynamic aphasia in the context of frontotemporal dementia. Our patients' insights into their verbal difficulty tend to support the notion that dynamic aphasia results from interference at the stage where thoughts are converted into verbal messages-that is, the thought-verbal interface.

Optimizing Use of Neuroimaging Tools in Evaluation of Prodromal Alzheimer's Disease and Related Disorders

Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by preclinical, pre-dementia, and dementia phases. Progression of the disease leads to cognitive decline and is associated with loss of functional independence, personality changes, and behavioral disturbances. Current guidelines for AD diagnosis include the use of neuroimaging tools as biomarkers for identifying and monitoring pathological changes. Various imaging modalities, namely magnetic resonance imaging (MRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and PET with amyloid-beta tracers are available to facilitate early accurate diagnoses. Enhancing diagnosis in the early stages of the disease can allow for timely interventions that can delay progression of the disease. This paper will discuss the characteristic findings associated with each of the imaging tools for patients with AD, with a focus on FDG-PET due to its established accuracy in assisting with the differential diagnosis of dementia and discussion of other methods including MRI. Diagnostically-relevant features to aid clinicians in making a differential diagnosis will also be pointed out and multimodal imaging will be reviewed. We also discuss the role of quantification software in interpretation of brain imaging. Lastly, to guide evaluation of patients presenting with cognitive deficits, an algorithm for optimal integration of these imaging tools will be shared. Molecular imaging modalities used in dementia evaluations hold promise toward identifying AD-related pathology before symptoms are fully in evidence. The work describes state of the art functional and molecular imaging methods for AD. It will also overview a clinically applicable quantitative method for reproducible assessments of such scans in the early identification of AD.

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.

A longitudinal observation of brain structure between AD and FTLD

INTRODUCTION

Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) are the leading causes of dementia. To better understand the disease development of cognitive function and anatomical structure in AD and FTLD, we analyzed the changes in brain volume by MRI and the psychological test results. Here, we report a dynamic observation of brain structure.

METHODS

Thirteen patients diagnosed with probable AD by the 2011 NIA-AA criteria and eight FTLD patients diagnosed by the FTLD criteria underwent MRI at baseline. All subjects were rescanned after 5 months to 3 years of follow-up. The anatomic changes on T1-weighted imaging of each subject were measured, and the separate changes in the two groups and the differences in the changes between AD and FTLD were analyzed.

RESULTS

In AD patients, the anterior and posterior horns of the lateral ventricle and lateral fissure enlarged progressively (p < 0.001). The volume of the regions, including the medial and lateral temporal lobe, especially the parahippocampal gyrus, and the frontal lobe decreased significantly as the disease progressed (p < 0.001). Additionally, the volume of white matter in the frontal, parietal, temporal lobe and cerebellum decreased in a relatively symmetric pattern (p < 0.001). In FTLD patients, the anterior horn of the lateral ventricle, lateral fissure, cerebral longitudinal fissure, external space of the orbitofrontal cortex, and mesencephalon surrounding the cisterna were enlarged (p < 0.005), while regions including the left frontal lobe, anterior cingulate cortex, basal ganglia (especially the left basal ganglia), left lateral temporal lobe and inferior cerebellar vermis decreased as the disease progressed (p < 0.005). Regarding the differences between AD and FTLD, atrophy of the frontal lobe and bilateral basal ganglia was more significant in FTLD than in AD (p < 0.01). In addition, enlargements of the anterior horn of the lateral ventricle, left lateral fissure and interpeduncular cistern were more significant in FTLD patients than in AD patients (p < 0.01).

CONCLUSIONS

These findings suggest that AD and FTLD have distinctly different atrophy patterns: AD patients show diffuse atrophy while FTLD patients show an asymmetrical focal atrophy pattern, which might explain the relatively better and longer preservation of daily living function in FTLD patients.

Diagnosis of Alzheimer's Disease in Developed and Developing Countries: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

BACKGROUND

The present systematic review and meta-analysis of diagnostic test accuracy summarizes the last three decades in advances on diagnosis of Alzheimer's disease (AD) in developed and developing countries.

OBJECTIVE

To determine the accuracy of biomarkers in diagnostic tools in AD, for example, cerebrospinal fluid, positron emission tomography (PET), and magnetic resonance imaging (MRI), etc.

METHODS

The authors searched PubMed for published studies from 1990 to April 2020 on AD diagnostic biomarkers. 84 published studies were pooled and analyzed in this meta-analysis and diagnostic accuracy was compared by summary receiver operating characteristic statistics.

RESULTS

Overall, 84 studies met the criteria and were included in a meta-analysis. For EEG, the sensitivity ranged from 67 to 98%, with a median of 80%, 95% CI [75, 91], tau-PET diagnosis sensitivity ranged from 76 to 97%, with a median of 94%, 95% CI [76, 97]; and MRI sensitivity ranged from 41 to 99%, with a median of 84%, 95% CI [81, 87]. Our results showed that tau-PET diagnosis had higher performance as compared to other diagnostic methods in this meta-analysis.

CONCLUSION

Our findings showed an important discrepancy in diagnostic data for AD between developed and developing countries, which can impact global prevalence estimation and management of AD. Also, our analysis found a better performance for the tau-PET diagnostic over other methods to diagnose AD patients, but the expense of tau-PET scan seems to be the limiting factor in the diagnosis of AD in developing countries such as those found in Asia, Africa, and Latin America.

Accuracy of Biomarker Testing for Neuropathologically Defined Alzheimer Disease in Older Adults With Dementia

BACKGROUND

Biomarker accuracy for Alzheimer disease (AD) is uncertain.

PURPOSE

To summarize evidence on biomarker accuracy for classifying AD in older adults with dementia.

DATA SOURCES

Electronic bibliographic databases (searched from January 2012 to November 2019 for brain imaging and cerebrospinal fluid [CSF] tests and from inception to November 2019 for blood tests), ClinicalTrials.gov (to November 2019), and systematic review bibliographies.

STUDY SELECTION

English-language studies evaluating the accuracy of brain imaging, CSF testing, or blood tests for distinguishing neuropathologically defined AD from non-AD among older adults with dementia. Studies with low or medium risk of bias were analyzed.

DATA EXTRACTION

Two reviewers rated risk of bias. One extracted data; the other verified accuracy.

DATA SYNTHESIS

Fifteen brain imaging studies and 9 CSF studies met analysis criteria. Median sensitivity and specificity, respectively, were 0.91 and 0.92 for amyloid positron emission tomography (PET), 0.89 and 0.74 for ¹⁸F-labeled fluorodeoxyglucose (¹⁸F-FDG) PET, 0.64 and 0.83 for single-photon emission computed tomography, and 0.91 and 0.89 for medial temporal lobe atrophy on magnetic resonance imaging (MRI). Individual CSF biomarkers and ratios had moderate sensitivity (range, 0.62 to 0.83) and specificity (range, 0.53 to 0.69); in the few direct comparisons, β-amyloid 42 (Aβ42)/phosphorylated tau (p-tau) ratio, total tau (t-tau)/Aβ42 ratio, and p-tau appeared more accurate than Aβ42 and t-tau alone. Single studies suggested that amyloid PET, ¹⁸F-FDG PET, and CSF test combinations may add accuracy to clinical evaluation.

LIMITATIONS

Studies were small, biomarker cut points and neuropathologic AD were inconsistently defined, and methods with uncertain applicability to typical clinical settings were used. Few studies directly compared biomarkers, assessed test combinations, evaluated whether biomarkers improved classification accuracy when added to clinical evaluation, or reported harms.

CONCLUSION

In methodologically heterogeneous studies of uncertain applicability to typical clinical settings, amyloid PET, ¹⁸F-FDG PET, and MRI were highly sensitive for neuropathologic AD. Amyloid PET, ¹⁸F-FDG PET, and CSF test combinations may add accuracy to clinical evaluation.

PRIMARY FUNDING SOURCE

Agency for Healthcare Research and Quality. (PROSPERO: CRD42018117897).

Subtypes Based on Six Apolipoproteins in Non-Demented Elderly Are Associated with Cognitive Decline and Subsequent Tau Accumulation in Cerebrospinal Fluid

Apolipoproteins (APOs) have been implicated in the pathogenesis of Alzheimer's disease (AD). In the present study, we aimed to investigate if patterns of cerebrospinal fluid (CSF) APOs (APOA-I, APOC-III, APOD, APOE, APOH, and APOJ) levels are associated with changes over time in cognition, memory performance, neuroimaging markers, and AD-related pathologies (CSF Aβ42, t-tau, and p-tau) in non-demented older adults. At baseline, a total of 241 non-demented older adults with CSF APOs data was included in the present analysis. Hierarchical agglomerative cluster analysis including the six CSF APOs was carried out. Among non-demented older adults, we identified two clusters. Compare with the first cluster, the second cluster had higher levels of APOs in CSF. Additionally, the second cluster showed a more benign disease course, including slower cognitive decline and slower p-tau accumulation in CSF. Our data highlight the importance of APOs in the pathogenesis of AD.

The Cerebellum in Frontotemporal Dementia: a Meta-Analysis of Neuroimaging Studies

Frontotemporal dementia (FTD) is a neurodegenerative brain disorder primarily affecting the frontal and/or temporal lobes. Three main subtypes have been recognized: behavioural-variant FTD (bvFTD), semantic dementia (SD), and progressive nonfluent aphasia (PNFA), each of which has a distinct clinical and cognitive profile. Although the role of the cerebellum in cognition is increasingly accepted, knowledge of cerebellar changes across neuroimaging modalities and their contribution to behavioural and cognitive changes in FTD syndromes is currently scant. We conducted an anatomical/activation likelihood estimation (ALE) meta-analysis in 53 neuroimaging studies (structural MRI: 42; positron emission tomography: 6; functional MRI: 4; single-photon emission computed tomography: 1) to identify the patterns of cerebellar changes and their relations to profiles of behavioural and cognitive deficits in FTD syndromes. Overall, widespread bilateral cerebellar changes were found in FTD and notably the patterns were subtype specific. In bvFTD, ALE peaks were identified in the bilateral Crus, left lobule VI, right lobules VIIb and VIIIb. In SD, focal cerebellar changes were located in the left Crus I and lobule VI. A separate ALE meta-analysis on PNFA studies was not performed due to the limited number of studies available. In addition, the ALE analysis indicated that bilateral Crus I and Crus II were associated with behavioural disruption and cognitive dysfunction. This ALE meta-analysis provides the quantification of the location and extent of cerebellar changes across the main FTD syndromes, which in turn provides evidence of cerebellar contributions to behavioural and cognitive changes in FTD. These results bring new insights into the mechanisms mediating FTD symptomatology.

Forty years of structural brain imaging in mental disorders: is it clinically useful or not?

Structural brain imaging was introduced into routine clinical practice more than 40 years ago with the hope that it would support the diagnosis and treatment of mental disorders. It is now widely used to exclude organic brain disease (eg, brain tumors, cardiovascular, and inflammatory processes) in mental disorders. However, questions have been raised about whether structural brain imaging is still needed today and whether it could also be clinically useful to apply new biostatistical methods, such as machine learning. Therefore, the current paper not only reviews structural findings in Alzheimer disease, depression, bipolar disorder, and schizophrenia but also discusses the role of structural imaging in supporting diagnostic, prognostic, and therapeutic processes in mental disorders. Thus, it attempts to answer the questions whether, after four decades of use, structural brain imaging is clinically useful in mental disorders or whether it will become so in the future.

Prominent auditory deficits in primary progressive aphasia: A case study

Aphasia typically is associated with comparable difficulties in written and spoken modalities of language expression and comprehension. In contrast, auditory verbal agnosia is the disproportionate difficulty comprehending spoken compared to written language, also typically greater than difficulties with spoken and written language expression, in the absence of a primary sensory deficit. The terms pure word deafness and auditory verbal agnosia are often used synonymously. However, the broader term of auditory agnosia more accurately reflects difficulty processing both speech and non-speech sounds whereas individuals with auditory verbal agnosia (pure word deafness) have preserved processing of environmental sounds. Auditory agnosia is reported in the stroke literature, but rarely reported in progressive neurologic disorders. Here, we report a case of a woman who presented with what is best described as a prominent auditory deficit in the context of an initially unclassifiable, or mixed, primary progressive aphasia (PPA) with accompanying apraxia of speech. Her clinical presentation shared features with auditory agnosia, although sensory functioning was not formally assessed. We report clinical and neuroimaging data spanning 6 years and subsequent autopsy results. She presented at 65 years of age, 5 years post onset of symptoms that included insidious and progressive difficulties thinking of words, constructing sentences, pronouncing words, and understanding instructions. She had disproportionate difficulty with comprehension of spoken compared to written language. She eventually developed features of the nonfluent/agrammatic variant of PPA, as well as an apraxia of speech. Imaging with [18F]-fluorodeoxyglucose (FDG)-PET revealed progression of bilateral (left greater than right) hypometabolism involving the frontal, temporal (predominantly the lateral superior gyrus), and parietal lobes, that eventually included the supplementary motor area, anterior cingulate, and caudate. Autopsy revealed pathological lesions consistent with corticobasal degeneration.

In vivo hippocampal subfield shape related to TDP-43, amyloid beta, and tau pathologies

Despite advances in the development of biomarkers for Alzheimer's disease (AD), accurate ante-mortem diagnosis remains challenging because a variety of neuropathologic disease states can coexist and contribute to the AD dementia syndrome. Here, we report a neuroimaging study correlating hippocampal deformity with regional AD and transactive response DNA-binding protein of 43 kDA pathology burden. We used hippocampal shape analysis of ante-mortem T1-weighted structural magnetic resonance imaging images of 42 participants from two longitudinal cohort studies conducted by the Rush Alzheimer's Disease Center. Surfaces were generated for the whole hippocampus and zones approximating the underlying subfields using a previously developed automated image-segmentation pipeline. Multiple linear regression models were constructed to correlate the shape with pathology measures while accounting for covariates, with relationships mapped out onto hippocampal surface locations. A significant relationship existed between higher paired helical filaments-tau burden and inward hippocampal shape deformity in zones approximating CA1 and subiculum which persisted after accounting for coexisting pathologies. No significant patterns of inward surface deformity were associated with amyloid-beta or transactive response DNA-binding protein of 43 kDA after including covariates. Our findings indicate that hippocampal shape deformity measures in surface zones approximating CA1 may represent a biomarker for postmortem AD pathology.

Structural and Functional Magnetic Resonance Imaging of Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia after Alzheimer's disease (AD). Although diagnosis may be challenging, there is increasing evidence that the use of biomarkers according to 2017 revised criteria for diagnosis and management of dementia with Lewy bodies can increase diagnostic accuracy. Apart from nuclear medicine techniques, various magnetic resonance imaging (MRI) techniques have been utilized in attempt to enhance diagnostic accuracy. This chapter reviews structural, functional and diffusion MRI studies in DLB cohorts being compared to healthy controls, AD or dementia in Parkinson's disease (PDD). We also included relatively new MRI methods that may have potential to identify early DLB subjects and aim at examining brain iron and neuromelanin.

Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability

Frontotemporal dementia (FTD) is difficult to diagnose, due to its heterogeneous nature and overlap in symptoms with primary psychiatric disorders. Brain MRI for atrophy is a key biomarker but lacks sensitivity in the early stage. Morphometric MRI-based measures and machine learning techniques are a promising tool to improve diagnostic accuracy. Our aim was to review the current state of the literature using morphometric MRI to classify FTD and assess its applicability for clinical practice. A search was completed using Pubmed and PsychInfo of studies which conducted a classification of subjects with FTD from non-FTD (controls or another disorder) using morphometric MRI metrics on an individual level, using single or combined approaches. 28 relevant articles were included and systematically reviewed following PRISMA guidelines. The studies were categorized based on the type of FTD subjects included and the group(s) against which they were classified. Studies varied considerably in subject selection, MRI methodology, and classification approach, and results are highly heterogeneous. Overall many studies indicate good diagnostic accuracy, with higher performance when differentiating FTD from controls (highest result was accuracy of 100%) than other dementias (highest result was AUC of 0.874). Very few machine learning algorithms have been tested in prospective replication. In conclusion, morphometric MRI with machine learning shows potential as an early diagnostic biomarker of FTD, however studies which use rigorous methodology and validate findings in an independent real-life cohort are necessary before this method can be recommended for use clinically.

Radiological-Pathological Correlation in Alzheimer's Disease: Systematic Review of Antemortem Magnetic Resonance Imaging Findings

BACKGROUND

The standard method of ascertaining Alzheimer's disease (AD) remains postmortem assessment of amyloid plaques and neurofibrillary degeneration. Vascular pathology, Lewy bodies, TDP-43, and hippocampal sclerosis are frequent comorbidities. There is therefore a need for biomarkers that can assess these etiologies and provide a diagnosis in vivo.

OBJECTIVE

We conducted a systematic review of published radiological-pathological correlation studies to determine the relationship between antemortem magnetic resonance imaging (MRI) and neuropathological findings in AD.

METHODS

We explored PubMed in June-July 2015 using "Alzheimer's disease" and combinations of radiological and pathological terms. After exclusion following screening and full-text assessment of the 552 extracted manuscripts, three others were added from their reference list. In the end, we report results based on 27 articles.

RESULTS

Independently of normal age-related brain atrophy, AD pathology is associated with whole-brain and hippocampal atrophy and ventricular expansion as observed on T1-weighted images. Moreover, cerebral amyloid angiopathy and cortical microinfarcts are also related to brain volume loss in AD. Hippocampal sclerosis and TDP-43 are associated with hippocampal and medial temporal lobe atrophy, respectively. Brain volume loss correlates more strongly with tangles than with any other pathological finding. White matter hyperintensities observed on proton density, T2-weighted and FLAIR images are strongly related to vascular pathologies, but are also associated with other histological changes such as gliosis or demyelination.

CONCLUSION

Cerebral atrophy and white matter changes in the living brain reflect underlying neuropathology and may be detectable using antemortem MRI. In vivo MRI may therefore be an avenue for AD pathological staging.

Clinical validity of medial temporal atrophy as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework

Research criteria for Alzheimer's disease recommend the use of biomarkers for diagnosis, but whether biomarkers improve the diagnosis in clinical routine has not been systematically assessed. The aim is to evaluate the evidence for use of medial temporal lobe atrophy (MTA) as a biomarker for Alzheimer's disease at the mild cognitive impairment stage in routine clinical practice, with an adapted version of the 5-phase oncology framework for biomarker development. A literature review on visual assessment of MTA and hippocampal volumetry was conducted with other biomarkers addressed in parallel reviews. Ample evidence is available for phase 1 (rationale for use) and phase 2 (discriminative ability between diseased and control subjects). Phase 3 (early detection ability) is partly achieved: most evidence is derived from research cohorts or clinical populations with short follow-up, but validation in clinical mild cognitive impairment cohorts is required. In phase 4, only the practical feasibility has been addressed for visual rating of MTA. The rest of phase 4 and phase 5 have not yet been addressed.

Patterns of atrophy in pathologically confirmed dementias: a voxelwise analysis

OBJECTIVE

Imaging is recommended to support the clinical diagnoses of dementias, yet imaging research studies rarely have pathological confirmation of disease. This study aims to characterise patterns of brain volume loss in six primary pathologies compared with controls and to each other.

METHODS

One hundred and eighty-six patients with a clinical diagnosis of dementia and histopathological confirmation of underlying pathology, and 73 healthy controls were included in this study. Voxel-based morphometry, based on ante-mortem T1-weighted MRI, was used to identify cross-sectional group differences in brain volume.

RESULTS

Early-onset and late-onset Alzheimer's disease exhibited different patterns of grey matter volume loss, with more extensive temporoparietal involvement in the early-onset group, and more focal medial temporal lobe loss in the late-onset group. The Presenilin-1 group had similar parietal involvement to the early-onset group with localised volume loss in the thalamus, medial temporal lobe and temporal neocortex. Lewy body pathology was associated with less extensive volume loss than the other pathologies, although precentral/postcentral gyri volume was reduced in comparison with other pathological groups. Tau and TDP43A pathologies demonstrated similar patterns of frontotemporal volume loss, although less extensive on the right in the 4-repeat-tau group, with greater parietal involvement in the TDP43A group. The TDP43C group demonstrated greater left anterior-temporal involvement.

CONCLUSIONS

Pathologically distinct dementias exhibit characteristic patterns of regional volume loss compared with controls and other dementias. Voxelwise differences identified in these cohorts highlight imaging signatures that may aid in the differentiation of dementia subtypes during life. The results of this study are available for further examination via NeuroVault (http://neurovault.org/collections/ADHMHOPN/).

Differential diagnosis between patients with probable Alzheimer's disease, Parkinson's disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features

The objective of this work was to develop and evaluate a classifier for differentiating probable Alzheimer’s disease (AD) from Parkinson’s disease dementia (PDD) or dementia with Lewy bodies (DLB) and from frontotemporal dementia, behavioral variant (bvFTD) based on quantitative electroencephalography (QEEG). We compared 25 QEEG features in 61 dementia patients (20 patients with probable AD, 20 patients with PDD or probable DLB (DLBPD), and 21 patients with bvFTD). Support vector machine classifiers were trained to distinguish among the three groups. Out of the 25 features, 23 turned out to be significantly different between AD and DLBPD, 17 for AD versus bvFTD, and 12 for bvFTD versus DLBPD. Using leave-one-out cross validation, the classification achieved an accuracy, sensitivity, and specificity of 100% using only the QEEG features Granger causality and the ratio of theta and beta1 band powers. These results indicate that classifiers trained with selected QEEG features can provide a valuable input in distinguishing among AD, DLB or PDD, and bvFTD patients. In this study with 61 patients, no misclassifications occurred. Therefore, further studies should investigate the potential of this method to be applied not only on group level but also in diagnostic support for individual subjects.

Alzheimer's disease: The influence of age on clinical heterogeneity through the human brain connectome

INTRODUCTION

One major factor that influences the heterogeneity of Alzheimer's disease (AD) is age: younger AD patients more frequently exhibit atypical forms of AD. We propose that this age-related heterogeneity can be understood better by considering age-related differences in atrophy in the context of large-scale brain networks subserving cognitive functions that contribute to memory.

METHODS

We examined data from 75 patients with mild AD dementia from Alzheimer's Disease Neuroimaging Initiative. These individuals were chosen because they have cerebrospinal fluid amyloid and p-tau levels in the range suggesting the presence of AD neuropathology, and because they were either younger than age 65 years early-onset AD (EOAD) or age 80 years or older late-onset AD (LOAD).

RESULTS

In the EOAD group, the most prominent atrophy was present in the posterior cingulate cortex, whereas in the LOAD group, atrophy was most prominent in the medial temporal lobe. Structural covariance analysis showed that the magnitude of atrophy in these epicenters is strongly correlated with a distributed atrophy pattern similar to distinct intrinsic connectivity networks in the healthy brain. An examination of memory performance in EOAD dementia versus LOAD dementia demonstrated relatively more prominent impairment in encoding in the EOAD group than in the LOAD group, with similar performance in memory storage in LOAD and EOAD but greater impairment in semantic memory in LOAD than in EOAD.

DISCUSSION

The observations provide novel insights about age as a major factor contributing to the heterogeneity in the topography of AD-related cortical atrophy.

Advanced magnetic resonance imaging of neurodegenerative diseases

Magnetic resonance imaging (MRI) is playing an increasingly important role in the study of neurodegenerative diseases, delineating the structural and functional alterations determined by these conditions. Advanced MRI techniques are of special interest for their potential to characterize the signature of each neurodegenerative condition and aid both the diagnostic process and the monitoring of disease progression. This aspect will become crucial when disease-modifying (personalized) therapies will be established. MRI techniques are very diverse and go from the visual inspection of MRI scans to more complex approaches, such as manual and automatic volume measurements, diffusion tensor MRI, and functional MRI. All these techniques allow us to investigate the different features of neurodegeneration. In this review, we summarize the most recent advances concerning the use of MRI in some of the most important neurodegenerative conditions, putting an emphasis on the advanced techniques.

Applied multimodal diagnostics in a case of presenile dementia

Background

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly. The possibility of disease-modifying strategies has evoked a need for early and accurate diagnosis. To improve the accuracy of the clinical diagnosis of AD, biomarkers like cerebrospinal fluid (CSF) and neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) have been incorporated into the diagnostic guidelines of AD.

Case presentation

In this case report we outline in reference to one of our patients with presenile dementia the current approaches to the diagnosis of AD. The patient was a 59-year old woman presenting with progressive memory decline. CSF-Aβ₄₂ was normal while P-tau was slightly increased. FDG-PET indicated a pattern typical for AD, amyloid-PET showed an extensive global amyloid load, and tau-PET depicted a pronounced hippocampal tracer accumulation. The MRI scan was rated as normal at routine diagnostics, however quantitative volumetric analysis revealed significant atrophy especially of the parietal lobe. The combination of biomarkers and neuroimaging techniques was therefore suggestive of an underlying AD pathology.

Conclusions

To enable early and accurate diagnosis of AD and thereby also patient recruitment for anti-tau or anti-β-amyloid therapeutic trials, a combination of biomarkers and neuroimaging techniques seems useful.

Immunity factor contributes to altered brain functional networks in individuals at risk for Alzheimer's disease: Neuroimaging-genetic evidence

Clusterin (CLU) is recognized as a secreted protein that is related to the processes of inflammation and immunity in the pathogenesis of Alzheimer's disease (AD). The effects of the risk variant of the C allele at the rs11136000 locus of the CLU gene are associated with variations in the brain structure and function. However, the relationship of the CLU-C allele to architectural disruptions in resting-state networks in amnestic mild cognitive impairment (aMCI) subjects (i.e., individuals with elevated risk of AD) remains relatively unknown. Using resting-state functional magnetic resonance imaging and an imaging genetic approach, this study investigated whether individual brain functional networks, i.e., the default mode network (DMN) and the task-positive network, were modulated by the CLU-C allele (rs11136000) in 50 elderly participants, including 26 aMCI subjects and 24 healthy controls. CLU-by-aMCI interactions were associated with the information-bridging regions between resting-state networks rather than with the DMN itself, especially in cortical midline regions. Interestingly, the complex communications between resting-state networks were enhanced in aMCI subjects with the CLU rs11136000 CC genotype and were modulated by the degree of memory impairment, suggesting a reconstructed balance of the resting-state networks in these individuals with an elevated risk of AD. The neuroimaging-genetic evidence indicates that immunity factors may contribute to alterations in brain functional networks in aMCI. These findings add to the evidence that the CLU gene may represent a potential therapeutic target for slowing disease progression in AD.

Evolving Evidence for the Value of Neuroimaging Methods and Biological Markers in Subjects Categorized with Subjective Cognitive Decline

There is evolving evidence that individuals categorized with subjective cognitive decline (SCD) are potentially at higher risk for developing objective and progressive cognitive impairment compared to cognitively healthy individuals without apparent subjective complaints. Interestingly, SCD, during advancing preclinical Alzheimer's disease (AD), may denote very early, subtle cognitive decline that cannot be identified using established standardized tests of cognitive performance. The substantial heterogeneity of existing SCD-related research data has led the Subjective Cognitive Decline Initiative (SCD-I) to accomplish an international consensus on the definition of a conceptual research framework on SCD in preclinical AD. In the area of biological markers, the cerebrospinal fluid signature of AD has been reported to be more prevalent in subjects with SCD compared to healthy controls; moreover, there is a pronounced atrophy, as demonstrated by magnetic resonance imaging, and an increased hypometabolism, as revealed by positron emission tomography, in characteristic brain regions affected by AD. In addition, SCD individuals carrying an apolipoprotein ɛ4 allele are more likely to display AD-phenotypic alterations. The urgent requirement to detect and diagnose AD as early as possible has led to the critical examination of the diagnostic power of biological markers, neurophysiology, and neuroimaging methods for AD-related risk and clinical progression in individuals defined with SCD. Observational studies on the predictive value of SCD for developing AD may potentially be of practical value, and an evidence-based, validated, qualified, and fully operationalized concept may inform clinical diagnostic practice and guide earlier designs in future therapy trials.

Biomarker clusters are differentially associated with longitudinal cognitive decline in late midlife

The ability to detect preclinical Alzheimer's disease is of great importance, as this stage of the Alzheimer's continuum is believed to provide a key window for intervention and prevention. As Alzheimer's disease is characterized by multiple pathological changes, a biomarker panel reflecting co-occurring pathology will likely be most useful for early detection. Towards this end, 175 late middle-aged participants (mean age 55.9 ± 5.7 years at first cognitive assessment, 70% female) were recruited from two longitudinally followed cohorts to undergo magnetic resonance imaging and lumbar puncture. Cluster analysis was used to group individuals based on biomarkers of amyloid pathology (cerebrospinal fluid amyloid-β42/amyloid-β40 assay levels), magnetic resonance imaging-derived measures of neurodegeneration/atrophy (cerebrospinal fluid-to-brain volume ratio, and hippocampal volume), neurofibrillary tangles (cerebrospinal fluid phosphorylated tau181 assay levels), and a brain-based marker of vascular risk (total white matter hyperintensity lesion volume). Four biomarker clusters emerged consistent with preclinical features of (i) Alzheimer's disease; (ii) mixed Alzheimer's disease and vascular aetiology; (iii) suspected non-Alzheimer's disease aetiology; and (iv) healthy ageing. Cognitive decline was then analysed between clusters using longitudinal assessments of episodic memory, semantic memory, executive function, and global cognitive function with linear mixed effects modelling. Cluster 1 exhibited a higher intercept and greater rates of decline on tests of episodic memory. Cluster 2 had a lower intercept on a test of semantic memory and both Cluster 2 and Cluster 3 had steeper rates of decline on a test of global cognition. Additional analyses on Cluster 3, which had the smallest hippocampal volume, suggest that its biomarker profile is more likely due to hippocampal vulnerability and not to detectable specific volume loss exceeding the rate of normal ageing. Our results demonstrate that pathology, as indicated by biomarkers, in a preclinical timeframe is related to patterns of longitudinal cognitive decline. Such biomarker patterns may be useful for identifying at-risk populations to recruit for clinical trials.

A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity

Alzheimer's disease (AD) researchers commonly use MRI as a quantitative measure of disease severity. Historically, hippocampal volume has been favored. Recently, “AD signature” measurements of gray matter (GM) volumes or cortical thicknesses have gained attention. Here, we systematically evaluate multiple thickness- and volume-based candidate-methods side-by-side, built using the popular FreeSurfer, SPM, and ANTs packages, according to the following criteria: (a) ability to separate clinically normal individuals from those with AD; (b) (extent of) correlation with head size, a nuisance covariatel (c) reliability on repeated scans; and (d) correlation with Braak neurofibrillary tangle stage in a group with autopsy. We show that volume- and thickness-based measures generally perform similarly for separating clinically normal from AD populations, and in correlation with Braak neurofibrillary tangle stage at autopsy. Volume-based measures are generally more reliable than thickness measures. As expected, volume measures are highly correlated with head size, while thickness measures are generally not. Because approaches to statistically correcting volumes for head size vary and may be inadequate to deal with this underlying confound, and because our goal is to determine a measure which can be used to examine age and sex effects in a cohort across a large age range, we thus recommend thickness-based measures. Ultimately, based on these criteria and additional practical considerations of run-time and failure rates, we recommend an AD signature measure formed from a composite of thickness measurements in the entorhinal, fusiform, parahippocampal, mid-temporal, inferior-temporal, and angular gyrus ROIs using ANTs with input segmentations from SPM12.

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Evaluate thickness- and volume-based quantitative measures of AD severity

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Volume- and thickness-based measures perform similarly for separating by diagnosis.

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Volume-based measures are correlated with head size; thickness-based mostly aren't.

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We recommend an AD signature measure formed from cortical thickness measures.

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We recommend thicknesses using ANTs software with input segmentations from SPM12.

White matter hyperintensities and imaging patterns of brain ageing in the general population

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.

Mixed tau and TDP-43 pathology in a patient with unclassifiable primary progressive aphasia

Classifying primary progressive aphasia (PPA) into variants that may predict the underlying pathology is important. However, some PPA patients cannot be classified. A 78-year-old woman had unclassifiable PPA characterized by anomia, dysarthria, and apraxia of speech without agrammatism. Magnetic resonance imaging revealed left mesial temporal atrophy and 18-flourodeoxy-glucose positron emission tomography showed left anterior temporal and posterior frontal (premotor) hypometabolism. Autopsy revealed a mixed tauopathy (argyrophilic grain disease) and transactive response-DNA-binding-protein-43 proteinopathy. Dual pathologies may explain the difficulty classifying some PPA patients and recognizing this will be important as new imaging techniques (particularly tau-positron emission tomography) are introduced and patients begin enrollment in clinical trials targeting the underlying proteinopathy.

Postmortem imaging and neuropathologic correlations

Postmortem imaging refers to scanning autopsy specimens using magnetic resonance imaging (MRI) or optical imaging. This chapter summarizes postmortem imaging and its usefulness in brain mapping. Standard in vivo MRI has limited resolution due to time constraints and does not deliver cortical boundaries (e.g., Brodmann areas). Postmortem imaging offers a means to obtain ultra-high-resolution images with appropriate contrast for delineating cortical regions. Postmortem imaging provides the ability to validate MRI properties against histologic stained sections. This approach has enabled probabilistic mapping that is based on ex vivo MRI contrast, validated to histology, and subsequently mapped on to an in vivo model. This chapter emphasizes structural imaging, which can be validated with histologic assessment. Postmortem imaging has been applied to neuropathologic studies as well. This chapter includes many ex vivo studies, but focuses on studies of the medial temporal lobe, often involved in neurologic disease. New research using optical imaging is also highlighted.

Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis

Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.

The Relationship Between Medial Temporal Lobe Atrophy and Cognitive Impairment in Patients With Dementia With Lewy Bodies

BACKGROUND

The relationship between medial temporal lobe atrophy (MTA) and cognitive impairment in patients with dementia with Lewy bodies (DLB) remains unclear. We examined this relationship using voxel-based specific regional analysis system for Alzheimer disease (VSRAD) advance software, which allowed us to quantify the degree of MTA on images obtained from magnetic resonance imaging (MRI) scans.

METHODS

Thirty-seven patients diagnosed with DLB were recruited and scanned with a 1.5 Tesla MRI scanner. All MRI data were analyzed using VSRAD advance. The target volume of interest (VOI) included the entire region of the entorhinal cortex, hippocampus, and amygdala. The degree of MTA was obtained from the averaged positive z-score (Z score) on the target VOI, with higher scores indicating more severe MTA. Mini-Mental State Examination (MMSE) and the Revised Hasegawa Dementia Scale (HDS-R), which strengthened the measures of memory and language more than MMSE, were used to assess the presence of cognitive impairment.

RESULTS

A negative correlation was found between the Z score and MMSE total scores or the HDS-R total scores. A stepwise multiple regression analysis performed to adjust the covariate effects of sex, age, the onset age of the disease, duration of DLB, years of education, and donepezil treatment showed that the HDS-R total scores were independently associated with the Z score, whereas MMSE total scores were not.

CONCLUSIONS

These results suggest that MTA is related to cognitive impairment in patients with DLB, particularly the regions of orientation, immediate and delayed recall, and word fluency.

Suspected non-AD pathology in mild cognitive impairment

We aim to better characterize mild cognitive impairment (MCI) patients with suspected non-Alzheimer's disease (AD) pathology (SNAP) based on their longitudinal outcome, cognition, biofluid, and neuroimaging profile. MCI participants (n = 361) from ADNI-GO/2 were designated "amyloid positive" with abnormal amyloid-beta 42 levels (AMY+) and "neurodegeneration positive" (NEU+) with abnormal hippocampal volume or hypometabolism using fluorodeoxyglucose-positron emission tomography. SNAP was compared with the other MCI groups and with AMY- controls. AMY-NEU+/SNAP, 16.6%, were older than the NEU- groups but not AMY- controls. They had a lower conversion rate to AD after 24 months than AMY+NEU+ MCI participants. SNAP-MCI participants had similar amyloid-beta 42 levels, florbetapir and tau levels, but larger white matter hyperintensity volumes than AMY- controls and AMY-NEU- MCI participants. SNAP participants performed worse on all memory domains and on other cognitive domains, than AMY-NEU- participants but less so than AMY+NEU+ participants. Subthreshold levels of cerebral amyloidosis are unlikely to play a role in SNAP-MCI, but pathologies involving the hippocampus and cerebrovascular disease may underlie the neurodegeneration and cognitive impairment in this group.

Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection

Alzheimer's disease is a progressive neurodegenerative disease that typically manifests clinically as an isolated amnestic deficit that progresses to a characteristic dementia syndrome. Advances in neuroimaging research have enabled mapping of diverse molecular, functional, and structural aspects of Alzheimer's disease pathology in ever increasing temporal and regional detail. Accumulating evidence suggests that distinct types of imaging abnormalities related to Alzheimer's disease follow a consistent trajectory during pathogenesis of the disease, and that the first changes can be detected years before the disease manifests clinically. These findings have fuelled clinical interest in the use of specific imaging markers for Alzheimer's disease to predict future development of dementia in patients who are at risk. The potential clinical usefulness of single or multimodal imaging markers is being investigated in selected patient samples from clinical expert centres, but additional research is needed before these promising imaging markers can be successfully translated from research into clinical practice in routine care.

Neuropathologic correlates of regional brain volumes in a community cohort of older adults

The objective of this work was 2-fold: to generate macrostructural brain signatures of age-related neuropathologies in a community cohort of older adults and to determine the contribution of brain macrostructure to the variation in antemortem cognition after accounting for the contributions of neuropathologies and demographics. Cerebral hemispheres from 165 participants of 2 cohort studies of aging were imaged with magnetic resonance imaging ex vivo (mean age at death = 90 years; standard deviation = 6 years). The volumes of white matter and 42 gray matter regions were measured. The same hemispheres also underwent neuropathologic examination. Alzheimer's disease pathology was negatively associated with volumes of mainly temporal, frontal, and parietal gray matter regions, and with total white matter volume (p < 0.05, false discovery rate-corrected). A negative association was also detected between hippocampal sclerosis and volumes of the hippocampus, as well as other temporal and frontal gray matter regions (p < 0.05, false discovery rate-corrected). The volume of mainly medial temporal lobe regions explained an additional 5%-6% of the variation in antemortem cognition, above and beyond what was explained by neuropathologies and demographics.

Classifying dementia using local binary patterns from different regions in magnetic resonance images

Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain combined with a Random Forest classifier in an attempt to discern patients with Alzheimer's disease (AD), Lewy body dementia (LBD), and normal controls (NC). Analysis was conducted in areas with white matter lesions (WML) and all of white matter (WM). Results from 10-fold nested cross validation are reported as mean accuracy, precision, and recall with standard deviation in brackets. The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04). In the three-class problem AD versus LBD versus NC and the two-class problem AD versus LBD, we achieved 0.87 (0.08) and 0.74 (0.16), respectively. The performance using 3DT1 images was notably better than when using FLAIR images. The results from the WM region gave similar results as in the WML region. Our study demonstrates that LBP texture analysis in brain MR images can be successfully used for computer based dementia diagnosis.

What should we know about dementia in the 21st century? A Delphi consensus study

Background

Escalating numbers of people are experiencing dementia in many countries. With increasing consumer needs, there is anticipated growth in the numbers of people providing diagnostic evaluations, treatments, and care. Ensuring a consistent and contemporary understanding of dementia across all of these groups has become a critical issue. This study aimed to reach consensus among dementia experts from English speaking countries regarding essential and contemporary knowledge about dementia.

Methods

An online Delphi study was conducted to examine expert opinion concerning dementia knowledge with three rounds of data collection. A sample of dementia experts was selected by a panel of Australian experts, including a geriatrician and three professors of aged care. Purposive selection was initially undertaken with the sample expanded through snowballing. Dementia experts (N = 19) included geriatricians, psychologists, psychiatrists, neuroscientists, dementia advocates, and nurse academics from the United Kingdom, United States, and Australia. In the first round, these participants provided open-ended responses to questions determining what comprised essential knowledge about dementia. In the second round, responses were summarised into 66 discrete statements that participants rated on the basis of importance. In the third round, a rank-ordered list of the 66 statements and a group median were provided and participants rated the statements again. The degree of consensus regarding importance ratings was determined by assessing median, interquartile range, and proportion of experts scoring above predetermined thresholds. Correlation scores were calculated for each statement after the final round to identify changes in statement scores.

Results

The Delphi experts identified 36 statements about dementia that they considered essential to understanding the condition. Statements about care for a person experiencing dementia and their care giver represented the largest response category. Other statements, for which full or very high consensus was reached, related to dementia characteristics, symptoms and progression, diagnosis and assessment, and treatment and prevention.

Conclusions

These results summarise knowledge of dementia that is considered essential across expert representatives of key stakeholder groups from three countries. This information has implications for the delivery of care to people with the condition and the development of dementia education programs.