Structural MRI in frontotemporal dementia: comparisons between hippocampal volumetry, tensor-based morphometry and voxel-based morphometry

An imaging review of the hippocampus and its common pathologies

The hippocampus is a complex structure located in the mesial temporal lobe that plays a critical role in cognitive and memory-related processes. The hippocampal formation consists of the dentate gyrus, hippocampus proper, and subiculum, and its importance in the neural circuitry makes it a key anatomic structure to evaluate in neuroimaging studies. Advancements in imaging techniques now allow detailed assessment of hippocampus internal architecture and signal features that has improved identification and characterization of hippocampal abnormalities. This review aims to summarize the neuroimaging features of the hippocampus and its common pathologies. It provides an overview of the hippocampal anatomy on magnetic resonance imaging and discusses how various imaging techniques can be used to assess the hippocampus. The review explores neuroimaging findings related to hippocampal variants (incomplete hippocampal inversion, sulcal remnant and choroidal fissure cysts), and pathologies of neoplastic (astrocytoma and glioma, ganglioglioma, dysembryoplastic neuroepithelial tumor, multinodular and vacuolating neuronal tumor, and metastasis), epileptic (mesial temporal sclerosis and focal cortical dysplasia), neurodegenerative (Alzheimer's disease, progressive primary aphasia, and frontotemporal dementia), infectious (Herpes simplex virus and limbic encephalitis), vascular (ischemic stroke, arteriovenous malformation, and cerebral cavernous malformations), and toxic-metabolic (transient global amnesia and opioid-associated amnestic syndrome) etiologies.

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks

Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during development. However, few or no attempts have been made to propose a reproducible CAD development workflow for 3D MRI data. In this paper, we present the development of an easily reproducible and reliable CAD tool using the Clinica and MONAI frameworks that were developed to introduce standardized practices in medical imaging. A Deep Learning (DL) algorithm was trained to detect frontotemporal dementia (FTD) on data from the NIFD database to ensure reproducibility. The DL model yielded 0.80 accuracy (95% confidence intervals: 0.64, 0.91), 1 sensitivity, 0.6 specificity, 0.83 F1-score, and 0.86 AUC, achieving a comparable performance with other FTD classification approaches. Explainable AI methods were applied to understand AI behavior and to identify regions of the images where the DL model misbehaves. Attention maps highlighted that its decision was driven by hallmarking brain areas for FTD and helped us to understand how to improve FTD detection. The proposed standardized methodology could be useful for benchmark comparison in FTD classification. AI-based CAD tools should be developed with the goal of standardizing pipelines, as varying pre-processing and training methods, along with the absence of model behavior explanations, negatively impact regulators' attitudes towards CAD. The adoption of common best practices for neuroimaging data analysis is a step toward fast evaluation of efficacy and safety of CAD and may accelerate the adoption of AI products in the healthcare system.

Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes

This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating these changes. Participants (111 MCI, 73 normal cognition) underwent 3-T structural magnetic resonance imaging. The obtained images were assessed using voxel-based morphometry, including extraction of cerebral grey matter, analyses of statistical differences, and correlation analyses between cerebral grey matter and clinical cognitive scores in MCI. The CNN-based deep learning method was used to extract features of cerebral grey matter images. Compared to subjects with normal cognition, participants with MCI had grey matter atrophy mainly in the entorhinal cortex, frontal cortex, and bilateral frontotemporal lobes (p < 0.0001). This atrophy was significantly correlated with the decline in cognitive scores (p < 0.01). The accuracy, sensitivity, and specificity of the CNN model for identifying participants with MCI were 80.9%, 88.9%, and 75%, respectively. The area under the curve of the model was 0.891. These findings demonstrate that research based on brain morphology can provide an effective way for the clinical, non-invasive, objective evaluation and identification of early Alzheimer's disease.

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.

MRI-guided histology of TDP-43 knock-in mice implicates parvalbumin interneuron loss, impaired neurogenesis and aberrant neurodevelopment in amyotrophic lateral sclerosis-frontotemporal dementia

Amyotrophic lateral sclerosis and frontotemporal dementia are overlapping diseases in which MRI reveals brain structural changes in advance of symptom onset. Recapitulating these changes in preclinical models would help to improve our understanding of the molecular causes underlying regionally selective brain atrophy in early disease. We therefore investigated the translational potential of the TDP-43Q331K knock-in mouse model of amyotrophic lateral sclerosis-frontotemporal dementia using MRI. We performed in vivo MRI of TDP-43Q331K knock-in mice. Regions of significant volume change were chosen for post-mortem brain tissue analyses. Ex vivo computed tomography was performed to investigate skull shape. Parvalbumin neuron density was quantified in post-mortem amyotrophic lateral sclerosis frontal cortex. Adult mutants demonstrated parenchymal volume reductions affecting the frontal lobe and entorhinal cortex in a manner reminiscent of amyotrophic lateral sclerosis-frontotemporal dementia. Subcortical, cerebellar and brain stem regions were also affected in line with observations in pre-symptomatic carriers of mutations in C9orf72, the commonest genetic cause of both amyotrophic lateral sclerosis and frontotemporal dementia. Volume loss was also observed in the dentate gyrus of the hippocampus, along with ventricular enlargement. Immunohistochemistry revealed reduced parvalbumin interneurons as a potential cellular correlate of MRI changes in mutant mice. By contrast, microglia was in a disease activated state even in the absence of brain volume loss. A reduction in immature neurons was found in the dentate gyrus, indicative of impaired adult neurogenesis, while a paucity of parvalbumin interneurons in P14 mutant mice suggests that TDP-43Q331K disrupts neurodevelopment. Computerized tomography imaging showed altered skull morphology in mutants, further suggesting a role for TDP-43Q331K in development. Finally, analysis of human post-mortem brains confirmed a paucity of parvalbumin interneurons in the prefrontal cortex in sporadic amyotrophic lateral sclerosis and amyotrophic lateral sclerosis linked to C9orf72 mutations. Regional brain MRI changes seen in human amyotrophic lateral sclerosis-frontotemporal dementia are recapitulated in TDP-43Q331K knock-in mice. By marrying in vivo imaging with targeted histology, we can unravel cellular and molecular processes underlying selective brain vulnerability in human disease. As well as helping to understand the earliest causes of disease, our MRI and histological markers will be valuable in assessing the efficacy of putative therapeutics in TDP-43Q331K knock-in mice.

Hippocampal shape across the healthy lifespan and its relationship with cognition

The study of the hippocampus across the healthy adult lifespan has rendered inconsistent findings. While volumetric measurements have often been a popular technique for analysis, more advanced morphometric techniques have demonstrated compelling results that highlight the importance and improved specificity of shape-based measures. Here, the MAGeT Brain algorithm was applied on 134 healthy individuals aged 18-81 years old to extract hippocampal subfield volumes and hippocampal shape measurements, namely: local surface area (SA) and displacement. We used linear-, second- or third-order natural splines to examine the relationships between hippocampal measures and age. In addition, partial least squares analyses were performed to relate volume and shape measurements with cognitive and demographic information. Volumetric results indicated a relative preservation of the right cornus ammonis 1 with age and a global volume reduction linked with older age, female sex, lower levels of education and cognitive performance. Vertex-wise analysis demonstrated an SA preservation in the anterior hippocampus with a peak during the sixth decade, while the posterior hippocampal SA gradually decreased across lifespan. Overall, SA decrease was linked to older age, female sex and, to a lesser extent lower levels of education and cognitive performance. Outward displacement in the lateral hippocampus and inward displacement in the medial hippocampus were enlarged with older age, lower levels of cognition and education, indicating an accentuation of the hippocampal "C" shape with age. Taken together, our findings suggest that vertex-wise analyses have higher spatial specifity and that sex, education, and cognition are implicated in the differential impact of age on hippocampal subregions throughout its anteroposterior and medial-lateral axes. This article is part of the Virtual Special Issue titled COGNITIVE NEU- ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.

The pathoconnectivity network analysis of the insular cortex: A morphometric fingerprinting

Brain disorders tend to impact on many different regions in a typical way: alterations do not spread randomly; rather, they seem to follow specific patterns of propagation that show a strong overlap between different pathologies. The insular cortex is one of the brain areas more involved in this phenomenon, as it seems to be altered by a wide range of brain diseases. On these grounds we thoroughly investigated the impact of brain disorders on the insular cortices analyzing the patterns of their structural co-alteration. We therefore investigated, applying a network analysis approach to meta-analytic data, 1) what pattern of gray matter alteration is associated with each of the insular cortex parcels; 2) whether or not this pattern correlates and overlaps with its functional meta-analytic connectivity; and, 3) the behavioral profile related to each insular co-alteration pattern. All the analyses were repeated considering two solutions: one with two clusters and another with three. Our study confirmed that the insular cortex is one of the most altered cerebral regions among the cortical areas, and exhibits a dense network of co-alteration including a prevalence of cortical rather than sub-cortical brain regions. Regions of the frontal lobe are the most involved, while occipital lobe is the less affected. Furthermore, the co-alteration and co-activation patterns greatly overlap each other. These findings provide significant evidence that alterations caused by brain disorders are likely to be distributed according to the logic of network architecture, in which brain hubs lie at the center of networks composed of co-altered areas. For the first time, we shed light on existing differences between insula sub-regions even in the pathoconnectivity domain.

Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging

INTRODUCTION

Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem.

METHODS

We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier.

RESULTS

Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%).

DISCUSSION

This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis.

Voxel-based analysis and multivariate pattern analysis of diffusion tensor imaging study in anti-NMDA receptor encephalitis

PURPOSE

This study aimed to investigate brain white matter (WM) changes and their relationship to cognition in patients with anti-N-methyl-D-aspartate (anti-NMDA) receptor encephalitis. Multivariate pattern analysis (MVPA) was used to explore brain regions that play an important role in classification.

METHODS

Fifteen patients and fifteen controls underwent Montreal Cognitive Assessment (MoCA) and diffusion tensor imaging. Based on fractional anisotropy (FA) and mean diffusivity (MD) for MVPA classification, the weights of each brain region were calculated.

RESULTS

Compared with the controls, the patients showed an FA reduction in right middle temporal gyrus, left middle cerebellar peduncle, right praecuneus, and an MD increase in left medial temporal gyrus and left frontal lobe. The MoCA score for patients was lower than controls, especially in executive function, fluency, delayed recall and visual perception items. The FA value of right praecuneus was positively correlated with total MoCA score and fluency score. The MD of left frontal lobe was negatively correlated with total MoCA score, and MD of the left medial temporal gyrus was positively correlated with delayed recall. The accuracy, sensitivity and specificity of classification based on FA were 70%, 60% and 80%, respectively. Based on MD, they were each 80%. The brain regions with large weights from FA and MD overlap in temporal lobe, cerebellum and hippocampus.

CONCLUSIONS

These results suggest that WM changes are associated with cognitive deficits. MVPA based on FA and MD has good classification ability. Our study may provide new insights into the pathophysiological mechanisms of residual cognitive deficits.

The Use of Biomarkers and Genetic Screening to Diagnose Frontotemporal Dementia: Evidence and Clinical Implications

Within the wide range of neurodegenerative brain diseases, the differential diagnosis of frontotemporal dementia (FTD) frequently poses a challenge. Often, signs and symptoms are not characteristic of the disease and may instead reflect atypical presentations. Consequently, the use of disease biomarkers is of importance to correctly identify the patients. Here, we describe how neuropsychological characteristics, neuroimaging and neurochemical biomarkers and screening for causal gene mutations can be used to differentiate FTD from other neurodegenerative diseases as well as to distinguish between FTD subtypes. Summarizing current evidence, we propose a stepwise approach in the diagnostic evaluation. Clinical consensus criteria that take into account a full neuropsychological examination have relatively good accuracy (sensitivity [se] 75–95%, specificity [sp] 82–95%) to diagnose FTD, although misdiagnosis (mostly AD) is common. Structural brain MRI (se 70–94%, sp 89–99%) and FDG PET (se 47–90%, sp 68–98%) or SPECT (se 36–100%, sp 41–100%) brain scans greatly increase diagnostic accuracy, showing greater involvement of frontal and anterior temporal lobes, with sparing of hippocampi and medial temporal lobes. If these results are inconclusive, we suggest detecting amyloid and tau cerebrospinal fluid (CSF) biomarkers that can indicate the presence of AD with good accuracy (se 74–100%, sp 82–97%). The use of P-tau₁₈₁ and the Aβ_1–42/Aβ_1–40 ratio significantly increases the accuracy of correctly identifying FTD vs. AD. Alternatively, an amyloid brain PET scan can be performed to differentiate FTD from AD. When autosomal dominant inheritance is suspected, or in early onset dementia, mutation screening of causal genes is indicated and may also be offered to at-risk family members. We have summarized genotype–phenotype correlations for several genes that are known to cause familial frontotemporal lobar degeneration, which is the neuropathological substrate of FTD. The genes most commonly associated with this disease (C9orf72, MAPT, GRN, TBK1) are discussed, as well as some less frequent ones (CHMP2B, VCP). Several other techniques, such as diffusion tensor imaging, tau PET imaging and measuring serum neurofilament levels, show promise for future implementation as diagnostic biomarkers.

Detecting frontotemporal dementia syndromes using MRI biomarkers

Background

Diagnosing frontotemporal dementia may be challenging. New methods for analysis of regional brain atrophy patterns on magnetic resonance imaging (MRI) could add to the diagnostic assessment. Therefore, we aimed to develop automated imaging biomarkers for differentiating frontotemporal dementia subtypes from other diagnostic groups, and from one another.

Methods

In this retrospective multicenter cohort study, we included 1213 patients (age 67 ± 9, 48% females) from two memory clinic cohorts: 116 frontotemporal dementia, 341 Alzheimer's disease, 66 Dementia with Lewy bodies, 40 vascular dementia, 104 other dementias, 229 mild cognitive impairment, and 317 subjective cognitive decline. Three MRI atrophy biomarkers were derived from the normalized volumes of automatically segmented cortical regions: 1) the anterior vs. posterior index, 2) the asymmetry index, and 3) the temporal pole left index. We used the following performance metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. To account for the low prevalence of frontotemporal dementia we pursued a high specificity of 95%. Cross-validation was used in assessing the performance. The generalizability was assessed in an independent cohort (n = 200).

Results

The anterior vs. posterior index performed with an AUC of 83% for differentiation of frontotemporal dementia from all other diagnostic groups (Sensitivity = 59%, Specificity = 95%, positive likelihood ratio = 11.8, negative likelihood ratio = 0.4). The asymmetry index showed highest performance for separation of primary progressive aphasia and behavioral variant frontotemporal dementia (AUC = 85%, Sensitivity = 79%, Specificity = 92%, positive likelihood ratio = 9.9, negative likelihood ratio = 0.2), whereas the temporal pole left index was specific for detection of semantic variant primary progressive aphasia (AUC = 85%, Sensitivity = 82%, Specificity = 80%, positive likelihood ratio = 4.1, negative likelihood ratio = 0.2). The validation cohort provided corresponding results for the anterior vs. posterior index and temporal pole left index.

Conclusion

This study presents three quantitative MRI biomarkers, which could provide additional information to the diagnostic assessment and assist clinicians in diagnosing frontotemporal dementia.

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Quantitative MRI biomarkers (API, ASI, and TPL) for detection of FTD and its subtypes.

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API differentiated FTD from other diagnostic groups with AUC of 83%.

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ASI and TPL showed highest performance for PPA subtypes.

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A subcortical bvFTD subtype resembling AD atrophy pattern seems undetectable for MRI.

Frontotemporal dementia

Recent research reveals an overlap between frontotemporal dementia (FTD) and a variety of primary psychiatric disorders, challenging the artificial divisions between psychiatry and neurology. This chapter offers an overview of the clinical syndromes associated with FTD while describing links between these syndromes and neuroimaging. This is followed by a review of the neuropathology and genetic changes in the brain. We will illustrate the syndromic overlap that exists between FTD and several primary psychiatric disorders including bipolar affective disorder and schizophrenia. Emphasis will be placed on the behavioral variant of FTD (bvFTD), which is the common clinical syndrome seen with degeneration of the frontal lobes and is the most likely to be encountered in psychiatric settings.

The cognitive profile of behavioural variant FTD and its similarities with ALS: a systematic review and meta-analysis

Approximately 30% of patients with amyotrophic lateral sclerosis (ALS) have cognitive impairment and 8%-14% fulfil the criteria for behavioural variant frontotemporal dementia (bv-FTD). The cognitive profiles of ALS and bv-FTD have been reported to be comparable, but this has never been systematically investigated. We aimed to determine the cognitive profile of bv-FTD and examine its similarities with that of ALS, to provide evidence for the existence of a cognitive disease continuum encompassing bv-FTD and ALS. We therefore systematically reviewed neuropsychological studies on bv-FTD patients and healthy volunteers. Neuropsychological tests were divided in 10 cognitive domains and effect sizes were calculated for all domains and compared with the cognitive profile of ALS by means of a visual comparison and a Pearson's r correlation coefficient. We included 120 studies, totalling 2425 bv-FTD patients and 2798 healthy controls. All cognitive domains showed substantial effect sizes, indicating cognitive impairment in bv-FTD patients compared to healthy controls. The cognitive domains with the largest effect sizes were social cognition, verbal memory and fluency (1.77-1.53). The cognitive profiles of bv-FTD and ALS (10 cognitive domains, 1287 patients) showed similarities on visual comparison and a moderate correlation 0.58 (p=0.13). When social cognition, verbal memory, fluency, executive functions, language and visuoperception were considered, i.e. the cognitive profile of ALS, Pearson's r was 0.73 (p=0.09), which raised to 0.92 (p=0.03), when language was excluded in this systematic analysis of patients with a non-language subtype of FTD. The cognitive profile of bv-FTD consists of deficits in social cognition, verbal memory, fluency and executive functions and shows similarities with the cognitive profile of ALS. These findings support a cognitive continuum encompassing ALS and bv-FTD.

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.

Evaluating combinations of diagnostic tests to discriminate different dementia types

Introduction

We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia.

Methods

In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types.

Results

Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%.

Discussion

Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further.

Meta-analytic Review of Memory Impairment in Behavioral Variant Frontotemporal Dementia

OBJECTIVES

A meta-analysis of the extent, nature and pattern of memory performance in behavioral variant frontotemporal dementia (bvFTD). Multiple observational studies have challenged the relative sparing of memory in bvFTD as stated in the current diagnostic criteria.

METHODS

We performed a meta-analytic review covering the period 1967 to February 2017 of case-control studies on episodic memory in bvFTD versus control participants (16 studies, 383 patients, 603 control participants), and patients with bvFTD versus those with Alzheimer's disease (AD) (20 studies, 452 bvFTD, 874 AD). Differences between both verbal and non-verbal working memory, episodic memory learning and recall, and recognition memory were examined. Data were extracted from the papers and combined into a common metric measure of effect, Hedges' d.

RESULTS

Patients with bvFTD show large deficits in memory performance compared to controls (Hedges' d -1.10; 95% confidence interval [CI] [-1.23, -0.95]), but perform significantly better than patients with AD (Hedges' d 0.85; 95% CI [0.69, 1.03]). Learning and recall tests differentiate best between patients with bvFTD and AD (p<.01). There is 37-62% overlap in test scores between the two groups.

CONCLUSIONS

This study points to memory disorders in patients with bvFTD, with performance at an intermediate level between controls and patients with AD. This indicates that, instead of being an exclusion criterion for bvFTD diagnosis, memory deficits should be regarded as a potential integral part of the clinical spectrum. (JINS, 2018, 24, 593-605).

Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier

Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer's disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

Structural MRI and Amyloid PET Imaging for Prediction of Conversion to Alzheimer's Disease in Patients with Mild Cognitive Impairment: A Meta-Analysis

OBJECTIVE

The aim of this study was to explore the prognostic values of biomarkers of neurodegeneration as measured by magnetic resonance imaging (MRI) and amyloid burden as measured by amyloid positron emission tomography (PET) in predicting conversion to Alzheimer's disease (AD) in patients with mild cognitive impairment (MCI).

METHODS

PubMed and EMBASE databases were searched for structural MRI or amyloid PET imaging studies published between January 2000 and July 2014 that reported conversion to AD in patients with MCI. Means and standard deviations or individual numbers of biomarkers with positive or negative status at baseline and corresponding numbers of patients who had progressed to AD at follow-up were retrieved from each study. The effect size of each biomarker was expressed as Hedges's g.

RESULTS

Twenty-four MRI studies and 8 amyloid PET imaging studies were retrieved. 674 of the 1741 participants (39%) developed AD. The effect size for predicting conversion to AD was 0.770 [95% confidence interval (CI) 0.607-0.934] for across MRI and 1.316 (95% CI 0.920-1.412) for amyloid PET imaging (p<0.001). The effect size was 1.256 (95% CI 0.902-1.609) for entorhinal cortex volume from MRI.

CONCLUSION

Our study suggests that volumetric MRI measurement may be useful for the early detection of AD.

Using the Disease State Fingerprint Tool for Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease

BACKGROUND

Disease State Index (DSI) and its visualization, Disease State Fingerprint (DSF), form a computer-assisted clinical decision making tool that combines patient data and compares them with cases with known outcomes.

AIMS

To investigate the ability of the DSI to diagnose frontotemporal dementia (FTD) and Alzheimer's disease (AD).

METHODS

The study cohort consisted of 38 patients with FTD, 57 with AD and 22 controls. Autopsy verification of FTD with TDP-43 positive pathology was available for 14 and AD pathology for 12 cases. We utilized data from neuropsychological tests, volumetric magnetic resonance imaging, single-photon emission tomography, cerebrospinal fluid biomarkers and the APOE genotype. The DSI classification results were calculated with a combination of leave-one-out cross-validation and bootstrapping. A DSF visualization of a FTD patient is presented as an example.

RESULTS

The DSI distinguishes controls from FTD (area under the receiver-operator curve, AUC = 0.99) and AD (AUC = 1.00) very well and achieves a good differential diagnosis between AD and FTD (AUC = 0.89). In subsamples of autopsy-confirmed cases (AUC = 0.97) and clinically diagnosed cases (AUC = 0.94), differential diagnosis of AD and FTD performs very well.

CONCLUSIONS

DSI is a promising computer-assisted biomarker approach for aiding in the diagnostic process of dementing diseases. Here, DSI separates controls from dementia and differentiates between AD and FTD.

An evidence-based algorithm for the utility of FDG-PET for diagnosing Alzheimer's disease according to presence of medial temporal lobe atrophy

BACKGROUND

Imaging biomarkers for Alzheimer's disease include medial temporal lobe atrophy (MTLA) depicted on computed tomography (CT) or magnetic resonance imaging (MRI) and patterns of reduced metabolism on fluorodeoxyglucose positron emission tomography (FDG-PET).

AIMS

To investigate whether MTLA on head CT predicts the diagnostic usefulness of an additional FDG-PET scan.

METHOD

Participants had a clinical diagnosis of Alzheimer's disease (n = 37) or dementia with Lewy bodies (DLB; n = 30) or were similarly aged controls (n = 30). We visually rated MTLA on coronally reconstructed CT scans and, separately and blind to CT ratings, abnormal appearances on FDG-PET scans.

RESULTS

Using a pre-defined cut-off of MTLA ⩾5 on the Scheltens (0-8) scale, 0/30 controls, 6/30 DLB and 23/30 Alzheimer's disease had marked MTLA. FDG-PET performed well for diagnosing Alzheimer's disease v DLB in the low-MTLA group (sensitivity/specificity of 71%/79%), but in the high-MTLA group diagnostic performance of FDG-PET was not better than chance.

CONCLUSIONS

In the presence of a high degree of MTLA, the most likely diagnosis is Alzheimer's disease, and an FDG-PET scan will probably not provide significant diagnostic information. However, in cases without MTLA, if the diagnosis is unclear, an FDG-PET scan may provide additional clinically useful diagnostic information.

Differential diagnosis of neurodegenerative diseases using structural MRI data

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia.

Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making.

A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods.

The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.

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Differential diagnostics of dementias was studied using structural MRI data.

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504 patients with both T1 and FLAIR MRIs from five patient classes were evaluated.

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Different fully automatic quantification methods were compared and combined.

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Classification accuracy of 70.6% was obtained for 5-class classification problem.

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Combination of several quantification methods was needed for optimal accuracy.

Amnesia in frontotemporal dementia: shedding light on the Geneva historical data

Recent accumulated evidence indicates that episodic memory impairments could be part of the initial clinical expression of frontotemporal dementia (FTD). An early study on this issue was carried out by Constantinidis and colleagues in 1974, but it was subsequently overlooked for a long period of time. The scope of the present research was: (a) to explore the presence of early episodic memory impairments in the entire population of neuropathologically confirmed FTD patients from the Geneva brain collection; and (b) to expand the present insight on the association between the initial symptomatology and various characteristics, namely gender, age at onset, disease duration, and presence of Pick body neuropathology. A careful review of the records of 50 FTD patients hospitalized at the Department of Psychiatry of the Bel-Air Hospital, Geneva, Switzerland, from 1929 to 1999, was conducted. Further in-depth neuropathological analysis with novel immunohistological methods was carried out in 37 of the cases. The data showed that memory impairments were the first clinical symptom in several of the patients. In addition, this specific phenotypic expression of FTD was associated with the female gender, advanced age, and positive Pick body neuropathology. The current findings give the opportunity to historically vindicate the early work of Constantinidis and colleagues. In addition, the novel observations about the association of episodic memory impairments with the female gender and positive Pick body neuropathology add to the existing knowledge about this phenotypic expression of FTD.

Evaluating Behavior in Mouse Models of the Behavioral Variant of Frontotemporal Dementia: Which Test for Which Symptom?

The behavioral variant of frontotemporal dementia (bvFTD) is a neurodegenerative disease affecting people in their early sixties, characterized by dramatic changes in individual and social behavior. Despite the heterogeneity in the presentation of the clinical symptoms of bvFTD, some characteristic changes can be highlighted. Social disinhibition, changes in food preferences as well as loss of empathy and apathy are commonly described. This is accompanied by a characteristic and dramatic atrophy of the prefrontal cortex with the accumulation of protein aggregates in the neurons in this area. Several causative mutations in different genes have been discovered, allowing the development of transgenic animal models, especially mouse models. In mice, attention has been focused on the histopathological aspects of the pathology, but now studies are taking interest in assessing the behavioral phenotype of FTD models. Finding the right test corresponding to human symptoms is quite challenging, especially since the frontal cortex is much less developed in mice than in humans. Although challenging, the ability to detect relevant prefrontal cortex impairments in mice is crucial for therapeutic approaches. In this review, we aim to present the approaches that have been used to model the behavioral symptoms of FTD and to explore other relevant approaches to assess behavior involving the prefrontal cortex, as well as the deficits associated with FTD.

Joint assessment of white matter integrity, cortical and subcortical atrophy to distinguish AD from behavioral variant FTD: A two-center study

We investigated the ability of cortical and subcortical gray matter (GM) atrophy in combination with white matter (WM) integrity to distinguish behavioral variant frontotemporal dementia (bvFTD) from Alzheimer's disease (AD) and from controls using voxel-based morphometry, subcortical structure segmentation, and tract-based spatial statistics. To determine which combination of MR markers differentiated the three groups with the highest accuracy, we conducted discriminant function analyses. Adjusted for age, sex and center, both types of dementia had more GM atrophy, lower fractional anisotropy (FA) and higher mean (MD), axial (L1) and radial diffusivity (L23) values than controls. BvFTD patients had more GM atrophy in orbitofrontal and inferior frontal areas than AD patients. In addition, caudate nucleus and nucleus accumbens were smaller in bvFTD than in AD. FA values were lower; MD, L1 and L23 values were higher, especially in frontal areas of the brain for bvFTD compared to AD patients. The combination of cortical GM, hippocampal volume and WM integrity measurements, classified 97-100% of controls, 81-100% of AD and 67-75% of bvFTD patients correctly. Our results suggest that WM integrity measures add complementary information to measures of GM atrophy, thereby improving the classification between AD and bvFTD.

The free and cued selective reminding test distinguishes frontotemporal dementia from Alzheimer's disease

Memory impairment is often present in frontotemporal dementia (FTD) as a result of an inefficient use of learning strategies, sometimes leading to a misdiagnosis of Alzheimer's disease (AD). The Free and Cued Selective Reminding Test (FCSRT) is a memory test that controls attention and acquisition, by providing category cues in the learning process. The main goal of this study was to show the usefulness of the FCSRT in the distinction between behavioral (bv-) FTD and AD. Three matched subgroups of participants were considered: bv-FTD (n = 32), AD (n = 32), and a control group of healthy adults (n = 32). Results proved that while AD patients exhibited an overall impairment in FCSRT, bv-FTD subjects showed to benefit more from the controlled learning through category cues. AD patients were 25 times more likely to have an impaired FCSRT. The FCSRT has shown its utility in the distinction between bv-FTD and AD, therefore increasing the diagnostic accuracy.

The power of neuroimaging biomarkers for screening frontotemporal dementia

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer's disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase the feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data-driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N = 93) with cerebrospinal fluid, gray matter (GM) magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes required in the test classification analyses for each model. The data-driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89% sensitive and 89% specific) and required a lower minimum sample size (N = 26) relative to anatomical VOI and global measures. We conclude that a data-driven VOI approach using Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials.

Sequential distribution of pTDP-43 pathology in behavioral variant frontotemporal dementia (bvFTD)

We examined regional distribution patterns of phosphorylated 43-kDa TAR DNA-binding protein (pTDP-43) intraneuronal inclusions in frontotemporal lobar degeneration (FTLD). Immunohistochemistry was performed on 70 μm sections from FTLD-TDP autopsy cases (n = 39) presenting with behavioral variant frontotemporal dementia. Two main types of cortical pTDP-43 pathology emerged, characterized by either predominantly perikaryal pTDP-43 inclusions (cytoplasmic type, cFTLD) or long aggregates in dendrites (neuritic type, nFTLD). Cortical involvement in nFTLD was extensive and frequently reached occipital areas, whereas cases with cFTLD often involved bulbar somatomotor neurons and the spinal cord. We observed four patterns indicative of potentially sequential dissemination of pTDP-43: cases with the lowest burden of pathology (pattern I) were characterized by widespread pTDP-43 lesions in the orbital gyri, gyrus rectus, and amygdala. With increasing burden of pathology (pattern II) pTDP-43 lesions emerged in the middle frontal and anterior cingulate gyrus as well as in anteromedial temporal lobe areas, the superior and medial temporal gyri, striatum, red nucleus, thalamus, and precerebellar nuclei. More advanced cases showed a third pattern (III) with involvement of the motor cortex, bulbar somatomotor neurons, and the spinal cord anterior horn, whereas cases with the highest burden of pathology (pattern IV) were characterized by pTDP-43 lesions in the visual cortex. We interpret the four neuropathological patterns in bvFTD to be consistent with the hypothesis that pTDP-43 pathology can spread sequentially and may propagate along axonal pathways.

Adaptive modulation of adult brain gray and white matter to high altitude: structural MRI studies

The aim of this study was to investigate brain structural alterations in adult immigrants who adapted to high altitude (HA). Voxel-based morphometry analysis of gray matter (GM) volumes, surface-based analysis of cortical thickness, and Tract-Based Spatial Statistics analysis of white matter fractional anisotropy (FA) based on MRI images were conducted on 16 adults (20–22 years) who immigrated to the Qinghai-Tibet Plateau (2300–4400 m) for 2 years. They had no chronic mountain sickness. Control group consisted of 16 matched sea level subjects. A battery of neuropsychological tests was also conducted. HA immigrants showed significantly decreased GM volumes in the right postcentral gyrus and right superior frontal gyrus, and increased GM volumes in the right middle frontal gyrus, right parahippocampal gyrus, right inferior and middle temporal gyri, bilateral inferior ventral pons, and right cerebellum crus1. While there was some divergence in the left hemisphere, surface-based patterns of GM changes in the right hemisphere resembled those seen for VBM analysis. FA changes were observed in multiple WM tracts. HA immigrants showed significant impairment in pulmonary function, increase in reaction time, and deficit in mental rotation. Parahippocampal and middle frontal GM volumes correlated with vital capacity. Superior frontal GM volume correlated with mental rotation and postcentral GM correlated with reaction time. Paracentral lobule and frontal FA correlated with mental rotation reaction time. There might be structural modifications occurred in the adult immigrants during adaptation to HA. The changes in GM may be related to impaired respiratory function and psychological deficits.

MRI signatures of brain macrostructural atrophy and microstructural degradation in frontotemporal lobar degeneration subtypes

Brain magnetic resonance imaging (MRI) studies have demonstrated regional patterns of brain macrostructural atrophy and white matter microstructural alterations separately in the three major subtypes of frontotemporal lobar degeneration (FTLD), which includes behavioral variant frontotemporal dementia (bvFTD), semantic dementia (SD), and progressive nonfluent aphasia (PNFA). This study was to investigate to what extent the pattern of white matter microstructural alterations in FTLD subtypes mirrors the pattern of brain atrophy, and to compare the ability of various diffusion tensor imaging (DTI) indices in characterizing FTLD patients, as well as to determine whether DTI measures provide greater classification power for FTLD than measuring brain atrophy. Twenty-five patients with FTLD (13 with bvFTD, 6 with SD, and 6 with PNFA) and 19 healthy age-matched control subjects underwent both structural MRI and DTI scans. Measurements of regional brain atrophy were based on T1-weighted MRI data and voxel-based morphometry. Measurements of regional white matter degradation were based on voxelwise as well as regions-of-interest tests of DTI variations, expressed as fractional anisotropy, axial diffusivity, and radial diffusivity. Compared to controls, bvFTD, SD, and PNFA patients each exhibited characteristic regional patterns of brain atrophy and white matter damage. DTI overall provided significantly greater accuracy for FTLD classification than brain atrophy. Moreover, radial diffusivity was more sensitive in assessing white matter damage in FTLD than other DTI indices. The findings suggest that DTI in general and radial diffusivity in particular are more powerful measures for the classification of FTLD patients from controls than brain atrophy.