Diagnostic Accuracy of MRI and Additional [18F]FDG-PET for Behavioral Variant Frontotemporal Dementia in Patients with Late Onset Behavioral Changes

Data-driven analysis of regional brain metabolism in behavioral frontotemporal dementia and late-onset primary psychiatric diseases with frontal lobe syndrome: A PET/MRI study

Late-onset primary psychiatric disease (PPD) and behavioral frontotemporal dementia (bvFTD) present with a similar frontal lobe syndrome. We compare brain glucose metabolism in bvFTD and late-onset PPD and investigate the metabolic correlates of cognitive and behavioral disturbances through FDG-PET/MRI. We studied 37 bvFTD and 20 late-onset PPD with a mean clinical follow-up of three years. At baseline evaluation, metabolism of the dorsolateral, ventrolateral, orbitofrontal regions and caudate could classify the patients with a diagnostic accuracy of 91% (95% CI: 0.81-0.98%). 45% of PPD showed low-grade hypometabolism in the anterior cingulate and/or parietal regions. Frontal lobe metabolism was normal in 32% of genetic bvFTD and bvFTD with motor neuron signs. Hypometabolism of the frontal and caudate regions could help in distinguishing bvFTD from PPD, except in cases with motor neuron signs and/or genetic bvFTD for which brain metabolism may be less informative.

Major depression mistaken as frontotemporal dementia due to PET scan

Clinicians should be aware that the hypometabolism associated with depression can mimic frontotemporal dementia on PET.

Non-coding RNAs and neuroinflammation: implications for neurological disorders

Neuroinflammation is considered a balanced inflammatory response important in the intrinsic repair process after injury or infection. Under chronic states of disease, injury, or infection, persistent neuroinflammation results in a heightened presence of cytokines, chemokines, and reactive oxygen species that result in tissue damage. In the CNS, the surrounding microglia normally contain macrophages and other innate immune cells that perform active immune surveillance. The resulting cytokines produced by these macrophages affect the growth, development, and responsiveness of the microglia present in both white and gray matter regions of the CNS. Controlling the levels of these cytokines ultimately improves neurocognitive function and results in the repair of lesions associated with neurologic disease. MicroRNAs (miRNAs) are master regulators of the genome and subsequently control the activity of inflammatory responses crucial in sustaining a robust and acute immunological response towards an acute infection while dampening pathways that result in heightened levels of cytokines and chemokines associated with chronic neuroinflammation. Numerous reports have directly implicated miRNAs in controlling the abundance and activity of interleukins, TGF-B, NF-kB, and toll-like receptor-signaling intrinsically linked with the development of neurological disorders such as Parkinson's, ALS, epilepsy, Alzheimer's, and neuromuscular degeneration. This review is focused on discussing the role miRNAs play in regulating or initiating these chronic neurological states, many of which maintain the level and/or activity of neuron-specific secondary messengers. Dysregulated miRNAs present in the microglia, astrocytes, oligodendrocytes, and epididymal cells, contribute to an overall glial-specific inflammatory niche that impacts the activity of neuronal conductivity, signaling action potentials, neurotransmitter robustness, neuron-neuron specific communication, and neuron-muscular connections. Understanding which miRNAs regulate microglial activation is a crucial step forward in developing non-coding RNA-based therapeutics to treat and potentially correct the behavioral and cognitive deficits typically found in patients suffering from chronic neuroinflammation.

VentRa: distinguishing frontotemporal dementia from psychiatric disorders

The volume of the lateral ventricles is a reliable and sensitive indicator of brain atrophy and disease progression in behavioural variant frontotemporal dementia. In this study, we validate our previously developed automated tool using ventricular features (known as VentRa) for the classification of behavioural variant frontotemporal dementia versus a mixed cohort of neurodegenerative, vascular and psychiatric disorders from a clinically representative independent dataset. Lateral ventricles were segmented for 1110 subjects-14 behavioural variant frontotemporal dementia, 30 other frontotemporal dementia, 70 Lewy body disease, 898 Alzheimer's disease, 62 vascular brain injury and 36 primary psychiatric disorder from the publicly accessible National Alzheimer's Coordinating Center dataset to assess the performance of VentRa. Using ventricular features to discriminate behavioural variant frontotemporal dementia subjects from primary psychiatric disorders, VentRa achieved an accuracy rate of 84%, a sensitivity rate of 71% and a specificity rate of 89%. VentRa was able to identify behavioural variant frontotemporal dementia from a mixed age-matched cohort (i.e. other frontotemporal dementia, Lewy body disease, Alzheimer's disease, vascular brain injury and primary psychiatric disorders) and to correctly classify other disorders as 'not compatible with behavioral variant frontotemporal dementia' with a specificity rate of 83%. The specificity rates against each of the other individual cohorts were 80% for other frontotemporal dementia, 83% for Lewy body disease, 83% for Alzheimer's disease, 84% for vascular brain injury and 89% for primary psychiatric disorders. VentRa is a robust and generalizable tool with potential usefulness for improving the diagnostic certainty of behavioural variant frontotemporal dementia, particularly for the differential diagnosis with primary psychiatric disorders.

The use of synaptic biomarkers in cerebrospinal fluid to differentiate behavioral variant of frontotemporal dementia from primary psychiatric disorders and Alzheimer's disease

BACKGROUND

Lack of early molecular biomarkers in sporadic behavioral variants of frontotemporal dementia (bvFTD) and its clinical overlap with primary psychiatric disorders (PPD) hampers its diagnostic distinction. Synaptic dysfunction is an early feature in bvFTD and identification of specific biomarkers might improve its diagnostic accuracy. Our goal was to understand the differential diagnostic potential of cerebrospinal fluid (CSF) synaptic biomarkers in bvFTD versus PPD and their specificity towards bvFTD compared with Alzheimer's disease (AD) and controls. Additionally, we explored the association of CSF synaptic biomarkers with social cognition, cognitive performance, and disease severity in these clinical groups.

METHODS

Participants with probable bvFTD (n = 57), PPD (n = 71), AD (n = 60), and cognitively normal controls (n = 39) with available CSF, cognitive tests, and disease severity as frontotemporal lobar degeneration-modified clinical dementia rating scale (FTLD-CDR) were included. In a subset of bvFTD and PPD cases, Ekman 60 faces test scores for social cognition were available. CSF synaptosomal-associated protein 25 (SNAP25), neurogranin (Ng), neuronal pentraxin 2 (NPTX2), and glutamate receptor 4 (GluR4) were measured, along with neurofilament light (NfL), and compared between groups using analysis of covariance (ANCOVA) and logistic regression. Diagnostic accuracy was assessed using ROC analyses, and biomarker panels were selected using Wald's backward selection. Correlations with cognitive measures were performed using Pearson's partial correlation analysis.

RESULTS

NPTX2 concentrations were lower in the bvFTD group compared with PPD (p < 0.001) and controls (p = 0.003) but not compared with AD. Concentrations of SNAP25 (p < 0.001) and Ng (p < 0.001) were elevated in patients with AD versus those with bvFTD and controls. The modeled panel for differential diagnosis of bvFTD versus PPD consisted of NfL and NPTX2 (AUC = 0.96, CI: 0.93-0.99, p < 0.001). In bvFTD versus AD, the modeled panel consisted of NfL, SNAP25, Ng, and GluR4 (AUC = 0.86, CI: 0.79-0.92, p < 0.001). In bvFTD, lower NPTX2 (Pearson's r = 0.29, p = 0.036) and GluR4 (Pearson's r = 0.34, p = 0.014) concentrations were weakly associated with worse performance of total cognitive score. Lower GluR4 concentrations were also associated with worse MMSE scores (Pearson's r = 0.41, p = 0.002) as well as with worse executive functioning (Pearson's r = 0.36, p = 0.011) in bvFTD. There were no associations between synaptic markers and social cognition or disease severity in bvFTD.

CONCLUSION

Our findings of involvement of NTPX2 in bvFTD but not PPD contribute towards better understanding of bvFTD disease pathology.

Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI

Background

Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN).

Methods

Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach.

Results

The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping.

Conclusion

In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.

Identifying and Diagnosing TDP-43 Neurodegenerative Diseases in Psychiatry

Neuropsychiatric symptoms (NPS) are common manifestations of neurodegenerative disorders and are often early signs of those diseases. Among those neurodegenerative diseases, TDP-43 proteinopathies are an increasingly recognized cause of early neuropsychiatric manifestations. TDP-43-related diseases include frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), and Limbic-Predominant Age-Related TDP-43 Encephalopathy (LATE). The majority of TDP-43-related diseases are sporadic, but a significant proportion is hereditary, with progranulin (GRN) mutations and C9orf72 repeat expansions as the most common genetic etiologies. Studies reveal that NPS can be the initial manifestation of those diseases or can complicate disease course, but there is a lack of awareness among clinicians about TDP-43-related diseases, which leads to common diagnostic mistakes or delays. There is also emerging evidence that TDP-43 accumulations could play a role in late-onset primary psychiatric disorders. In the absence of robust biomarkers for TDP-43, the diagnosis remains primarily based on clinical assessment and neuroimaging. Given the association with psychiatric symptoms, clinical psychiatrists have a key role in the early identification of patients with TDP-43-related diseases. This narrative review provides a comprehensive overview of the pathobiology of TDP-43, resulting clinical presentations, and associated neuropsychiatric manifestations to help guide clinical practice.

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.

Diagnostic Instability Over Time in the Late-Onset Frontal Lobe Syndrome: When Can We Say it's FTD?

OBJECTIVES

Distinguishing sporadic behavioral variant of frontotemporal dementia (bvFTD) from late-onset primary psychiatric disorders (PPD) remains challenging with the lack of robust biomarkers. An early bvFTD misdiagnosis in PPD cases and vice-versa is common. Little is known about diagnostic (in)stability over longer period of time. We investigated diagnostic instability in a neuropsychiatric cohort up to 8 years after baseline visit and identified which clinical hallmarks contribute to diagnostic instability.

DESIGN

Diagnoses of participants of the late-onset frontal lobe (LOF) study were collected from the baseline visit (T0) and the 2-year follow-up visit (T2). Clinical outcomes were retrieved 5-8 years after baseline visit (Tfinal). Endpoint diagnoses were categorized into bvFTD, PPD and other neurological disorders (OND). We calculated the total amount of participants that switched diagnosis between T0-T2 and T2-Tfinal. Clinical records of participants that switched diagnosis were assessed.

RESULTS

Of the 137 patients that were included in the study, the final diagnoses at Tfinal were bvFTD 24.1% (n = 33), PPD 39.4% (n = 54), OND 33.6% (n = 46) and unknown 2.9% (n = 4). Between T0 and T2, a total of 29 (21.2%) patients switched diagnosis. Between T2 and Tfinal, 8 (5.8%) patients switched diagnosis. Prolonged follow-up identified few cases with diagnostic instability. Major contributors to diagnostic instability where a nonconverting diagnosis of possible bvFTD and a probable bvFTD diagnosis based on informant-based history and an abnormal FDG-PET scan whilst having a normal MRI.

CONCLUSION

Considering these lessons, a FTD diagnosis remains stable enough to conclude that 2 years is sufficient to say if a patient with late-life behavioral disorder has FTD.

Altered brain metabolism in frontotemporal dementia and psychiatric disorders: involvement of the anterior cingulate cortex

BACKGROUND

Behavioural symptoms and frontotemporal hypometabolism overlap between behavioural variant of frontotemporal dementia (bvFTD) and primary psychiatric disorders (PPD), hampering diagnostic distinction. Voxel-wise comparisons of brain metabolism might identify specific frontotemporal-(hypo)metabolic regions between bvFTD and PPD. We investigated brain metabolism in bvFTD and PPD and its relationship with behavioural symptoms, social cognition, severity of depressive symptoms and cognitive functioning.

RESULTS

Compared to controls, bvFTD showed decreased metabolism in the dorsal anterior cingulate cortex (dACC) (p < 0.001), orbitofrontal cortex (OFC), temporal pole, dorsolateral prefrontal cortex (dlPFC) and caudate, whereas PPD showed no hypometabolism. Compared to PPD, bvFTD showed decreased metabolism in the dACC (p < 0.001, p < 0.05_FWE), insula, Broca's area, caudate, thalamus, OFC and temporal cortex (p < 0.001), whereas PPD showed decreased metabolism in the motor cortex (p < 0.001). Across bvFTD and PPD, decreased metabolism in the temporal cortex (p < 0.001, p < 0.05_FWE), dACC and frontal cortex was associated with worse social cognition. Decreased metabolism in the dlPFC was associated with compulsiveness (p < 0.001). Across bvFTD, PPD and controls, decreased metabolism in the PFC and motor cortex was associated with executive dysfunctioning (p < 0.001).

CONCLUSIONS

Our findings indicate subtle but distinct metabolic patterns in bvFTD and PPD, most strongly in the dACC. The degree of frontotemporal and cingulate hypometabolism was related to impaired social cognition, compulsiveness and executive dysfunctioning. Our findings suggest that the dACC might be an important region to differentiate between bvFTD and PPD but needs further validation.

Frontotemporal Dementia, Where Do We Stand? A Narrative Review

Frontotemporal dementia (FTD) is a neurodegenerative disease of growing interest, since it accounts for up to 10% of middle-age-onset dementias and entails a social, economic, and emotional burden for the patients and caregivers. It is characterised by a (at least initially) selective degeneration of the frontal and/or temporal lobe, generally leading to behavioural alterations, speech disorders, and psychiatric symptoms. Despite the recent advances, given its extreme heterogeneity, an overview that can bring together all the data currently available is still lacking. Here, we aim to provide a state of the art on the pathogenesis of this disease, starting with established findings and integrating them with more recent ones. In particular, advances in the genetics field will be examined, assessing them in relation to both the clinical manifestations and histopathological findings, as well as considering the link with other diseases, such as amyotrophic lateral sclerosis (ALS). Furthermore, the current diagnostic criteria will be explored, including neuroimaging methods, nuclear medicine investigations, and biomarkers on biological fluids. Of note, the promising information provided by neurophysiological investigations, i.e., electroencephalography and non-invasive brain stimulation techniques, concerning the alterations in brain networks and neurotransmitter systems will be reviewed. Finally, current and experimental therapies will be considered.

Combining semi-quantitative rating and automated brain volumetry in MRI evaluation of patients with probable behavioural variant of fronto-temporal dementia: an added value for clinical practise?

PURPOSE

To evaluate the diagnostic value of combined semiquantitative and quantitative assessment of brain atrophy in the diagnostic workup of the behavioural-variant of frontotemporal dementia (bvFTD).

METHODS

Three neuroradiologists defined brain atrophy grading and identified atrophy pattern suggestive of bvFTD on 3D-T1 brain MRI of 112 subjects using a semiquantitative rating scale (Kipps'). A quantitative atrophy assessment was performed using two different automated software (Quantib® ND and Icometrix®). A combined semiquantitative and quantitative assessment of brain atrophy was made to evaluate the improvement in brain atrophy grading to identify probable bvFTD patients.

RESULTS

Observers' performances in the diagnosis of bvFTD were very good for Observer 1 (k value = 0.881) and 2 (k value = 0.867), substantial for Observer 3 (k value = 0.741). Semiquantitative atrophy grading of all the observers showed a moderate and a poor correlation with the volume values calculated by Icometrix® and by Quantib® ND, respectively. For the definition of neuroradiological signs presumptive of bvFTD, the use of Icometrix® software improved the diagnostic accuracy for Observer 1 resulting in an AUC of 0.974, and for Observer 3 resulting in a AUC of 0.971 (p-value < 0.001). The use of Quantib® ND software improved the diagnostic accuracy for Observer 1 resulting in an AUC of 0.974, and for Observer 3 resulting in a AUC of 0.977 (p-value < 0.001). No improvement was observed for Observer 2.

CONCLUSION

Combining semiquantitative and quantitative brain imaging evaluation allows to reduce discrepancies in the neuroradiological diagnostic workup of bvFTD by different readers.

Behavioral and psychological symptoms in neurodegenerative dementias: harbinger, follower, or constant collateral?

Neurodegenerative dementias such as the behavioral variant of frontotemporal dementia, Alzheimer’s disease, and Parkinson’s disease dementia are linked to various behavioral and psychological abnormalities. Whether these abnormalities precede, coincide or follow the onset of cognitive symptoms is still controversial in existing literature, with trajectories available so far dependent on types of dementia. The authors aim to review the different kinds of premorbid behavioral symptoms/personality traits associated with an increased risk of developing specific types of neurodegenerative dementia. Neuroticism has been associated with an increased risk of Alzheimer’s disease and late-onset behavioral abnormalities with the behavioral variant of frontotemporal dementia. The presence of obsessive–compulsive spectrum disorders in Parkinson’s disease dementia is also not rare. Analyzing this evidence, we propose “behavioral biomarkers” as neuroticism in Alzheimer’s disease, late-onset behavioral abnormalities in behavioral variant of frontotemporal dementia, and obsessive–compulsive traits in Parkinson’s disease dementia. These noninvasive behavioral biomarkers will be of immense help, particularly in developing countries, and will prevent the need for costlier investigations and aid in therapeutic strategies.

How to differentiate behavioral variant frontotemporal dementia from primary psychiatric disorders: practical aspects for the clinician

BACKGROUND

Due to the early and prominent behavioral changes which characterize behavioral variant frontotemporal dementia (bvFTD), patients are more likely to seek psychiatric help and are often initially diagnosed with a primary psychiatric disorder (PPD). Differentiating these conditions is critical because of the dramatically different outcomes, differences in patient management, family counseling and caregiver education.

OBJECTIVE

To propose a practical guide to distinguish between bvFTD and PDD.

METHODS

We conducted a non-systematic review of the published manuscripts in the field, including some previous investigations from our own group and work on which we have collaborated, and summarized the main findings and proposals that may be useful for neurological practice.

RESULTS

The reviewed literature suggests that a comprehensive clinical history, brief cognitive and neuropsychological evaluations, detailed neurological examination with special attention to motor alterations related to bvFTD, structural and functional neuroimaging evaluation, genetic investigation in selected cases, and assistance from a multidisciplinary team, including a neurologist and a psychiatrist with expertise in bvFTD, are very helpful in differentiating these conditions.

CONCLUSIONS

Although the clinician may commonly face great difficulty in differentiating between bvFTD and PPD, the use of appropriate tools in a systematic way and the availability of a well-trained multidisciplinary group can significantly increase diagnostic accuracy.

The bvFTD phenocopy syndrome: a case study supported by repeated MRI, [18F]FDG-PET and pathological assessment

A clinical syndrome with neuropsychiatric features of bvFTD without neuroimaging abnormalities and a lack of decline is a phenocopy of bvFTD (phFTD). Growing evidence suggests that psychological, psychiatric and environmental factors underlie phFTD. We describe a patient diagnosed with bvFTD prior to the revision of the diagnostic guidelines of FTD. Repeated neuroimaging was normal and there was no FTD pathology at autopsy, rejecting the diagnosis. We hypothesize on etiological factors that on hindsight might have played a role. This case report contributes to the understanding of phFTD and adds to the sparse literature of the postmortem assessment of phFTD.

Frontal hypometabolism in neurocognitive disorder with behavioral disturbance

Criteria for the behavioral variant of frontotemporal dementia (bvFTD) include decreased frontal metabolism. FDG-PET was used to investigate whether patients with neurocognitive disorder and behavioral disturbance (bvNCD) who did not fulfill three bvFTD criteria had characteristic brain metabolic pattern. Methods: Patients were referred from memory clinic to nuclear medicine for differential diagnosis of NCD with dysexecutive syndrome and predominant mild frontal atrophy. Patients were classified into two groups before FDG-PET, probable bvFTD (n = 25) or bvNCD (n = 27) when only two bvFTD criteria were met. Results: Voxel-based and multivariate PLS analyses of FDG-PET did not show significant between-group difference at inclusion. After 4.8 years of follow-up, most patients with probable bvFTD received the same diagnosis, 3 remained very stable and one participant was given a psychiatric diagnosis. Five patients with bvNCD fulfilled criteria for probable bvFTD at 4.4 years mean follow up, while 2 participants remained very stable and 3 received alternative neurological or psychiatric diagnoses. When initial FDG-PET were compared between groups stratified at follow up (26 bvFTD versus 17 bvNCD), there was a trend (p<.001uncorrected) for lower prefrontal with relatively preserved premotor metabolism in bvFTD compared to bvNCD. Twelve bvNCD participants had neuropsychological testing before inclusion. They all presented executive dysfunction and normal visuospatial performance, and most (n = 9) had memory encoding impairment. Conclusion: Frontal hypometabolism was observed in a dysexecutive presentation of frontal neurodegenerative disorder (bvNCD) that did not fulfill all clinical criteria for bvFTD.

MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia

INTRODUCTION

Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis.

METHODS

A total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation.

RESULTS

Average 10-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores.

CONCLUSION

Our results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database.

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.

Neuroimaging in Frontotemporal Lobar Degeneration: Research and Clinical Utility

Frontotemporal lobar dementia (FTLD) is a clinically and pathologically complex disease. Advances in neuroimaging techniques have provided a specialized set of tools to investigate underlying pathophysiology and identify clinical biomarkers that aid in diagnosis, prognostication, monitoring, and identification of appropriate endpoints in clinical trials. In this chapter, we review data discussing the utility of neuroimaging biomarkers in sporadic FTLD, with an emphasis on current and future clinical applications. Among those modalities readily utilized in clinical settings, T1-weighted structural magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) are best supported in differential diagnosis and as targets for clinical trial endpoints. However, a number of nonclinical neuroimaging modalities, including diffusion tensor imaging and resting-state functional connectivity MRI, show promise as biomarkers to predict progression and as clinical trial endpoints. Other neuroimaging modalities, including amyloid PET, Tau PET, and arterial spin labeling MRI, are also discussed, though more work is required to establish their utility in FTLD in clinical settings.

Molecular and Anatomical Imaging of Dementia With Lewy Bodies and Frontotemporal Lobar Degeneration

Dementia with Lewy bodies (DLB) and frontotemporal lobar degeneration (FTLD) are common causes of dementia. Early diagnosis of both conditions is challenging due to clinical and radiological overlap with other forms of dementia, particularly Alzheimer's disease (AD). Structural and functional imaging combined can aid differential diagnosis and help to discriminate DLB or FTLD from other forms of dementia. Imaging of DLB involves the use of ¹²³I-FP-CIT SPECT and ¹²³I-metaiodobenzylguanidine (¹²³I-MIBG), both of which have an established role distinguishing DLB from AD. AD is also characterised by more pronounced atrophy of the medial temporal lobe structures when compared to DLB and these can be assessed at MR using the Medial Temporal Atrophy Scale. ¹⁸F-FDG-PET is used as a supportive biomarker for the diagnoses of DLB and can distinguish DLB from AD with high accuracy. Polysomnography and electroencephalography also have established roles in the diagnoses of DLB. FTLD is a heterogenous group of neurodegenerative disorders characterised pathologically by abnormally aggregated proteins. Clinical subtypes include behavioral variant FTD (bvFTD), primary progressive aphasia (PPA), which can be subdivided into semantic variant PPA (svPPA) or nonfluent agrammatic PPA (nfaPPA) and FTD associated with motor neuron disease (FTD-MND). Structural imaging is often the first step in making an image supported diagnoses of FTLD. Regional patterns of atrophy can be assessed on MR and graded according to the global cortical atrophy scale. FTLD is typically associated with atrophy of the frontal and temporal lobes. The patterns of atrophy are associated with the specific clinical subtypes, underlying neuropathology and genetic mutations although there is significant overlap. ¹⁸F-FDG-PET is useful for distinguishing FTLD from other forms of dementia and focal areas of hypometabolism can often precede atrophy identified on structural MR imaging. There are currently no biomarkers with which to unambiguously diagnose DLB or FTLD and both conditions demonstrate a wide range of heterogeneity. A combined approach of structural and functional imaging improves diagnostic accuracy in both conditions.

Cost-effectiveness of neuroimaging technologies in management of psychiatric and insomnia disorders: A meta-analysis and prospective cost analysis

BACKGROUND

The optimal diagnostic strategy for patients with psychiatric and insomnia disorders has not been established yet.

PURPOSE

The purpose of this study was to perform cost-effectiveness analysis of six neuroimaging technologies in diagnosis of patients with psychiatric and insomnia disorders.

METHODS

An economic evaluation study was conducted in three parts, including a systematic review for determining diagnostic accuracy, a descriptive cross-sectional study with Activity-Based Costing (ABC) technique for tracing resource consumption, and a cost-effectiveness analysis using a short-term decision-analytic model.

RESULTS

In the first phase, 93 diagnostic accuracy studies were included in the systematic review. The accumulated results (meta-analysis) showed that the highest diagnostic accuracy for psychiatric and insomnia disorders was attributed to PET (sensitivity of 90% and specificity of 80%) and MRI (sensitivity of 76% and specificity of 78%) respectively. In the second phase of the study, we calculated the cost of each technology. The results showed that MRI has the lowest cost. Based on the results in the model of cost-effectiveness sMRI ($ 50.08 per accurate diagnosis) and MRI ($ 58.54 per accurate diagnosis) were more cost-effective neuroimaging technologies.

CONCLUSION

In psychiatric disorders, no single strategy was characterized by both low cost and high accuracy. However, MRI and PET scan had lower cost and higher accuracy for psychiatric disorders, respectively. MRI was the least costly with the highest diagnostic accuracy in insomnia disorders. Based on our model, sMRI in psychiatric disorders and MRI in insomnia disorders were the most cost-effective technologies.

Distinguishing Behavioral Variant Frontotemporal Dementia From Primary Psychiatric Disorders: A Review of Recently Published Consensus Recommendations From the Neuropsychiatric International Consortium for Frontotemporal Dementia

The behavioral variant of frontotemporal dementia (bvFTD) is the second most common cause of dementia for individuals <65 years old, but accurate diagnosis is often delayed for several years. While previous criteria have increased the ability of diagnosticians to distinguish between bvFTD and other neurocognitive disorders such as Alzheimer's disease, distinguishing bvFTD from a primary psychiatric disorder (PPD) has been more challenging. In early 2020, the Neuropsychiatric International Consortium for Frontotemporal Dementia published the first consensus recommendations to help clinicians distinguish between bvFTD and PPD. These recommendations were produced by a consortium of 45 scientists and clinicians from more than 15 different countries, who explored aspects of history taking, neuropsychological assessments, clinical scales, neuroimaging, CSF and serum biomarkers, and genetics. A multidisciplinary approach is encouraged throughout. In this article, the authors also review those consensus recommendations and highlight use of novel tests and techniques. Additionally, they indicate where further research is needed, including methods to assess the dissemination and implementation of these recommendations. In this way, future efforts by clinicians and researchers alike to improve accurate recognition of bvFTD are encouraged, thereby expanding opportunities for improved guidance and management.

Frontotemporal Dementia and Late-Onset Bipolar Disorder: The Many Directions of a Busy Road

It is a common pathway for patients with the behavioral variant of frontotemporal dementia (bvFTD) to be first misdiagnosed with a primary psychiatric disorder, a considerable proportion of them being diagnosed with bipolar disorder (BD). Conversely, not rarely patients presenting in late life with a first episode of mania or atypically severe depression are initially considered to have dementia before the diagnosis of late-onset BD is reached. Beyond some shared features that make these conditions particularly prone to confusion, especially in the elderly, the relationship between bvFTD and BD is far from simple. Patients with BD often have cognitive complaints as part of their psychiatric disorder but are at an increased risk of developing dementia, including FTD. Likewise, apathy and disinhibition, common features of depression and mania, respectively, are among the core features of the bvFTD syndrome, not to mention that depression may coexist with dementia. In this article, we take advantage of the current knowledge on the neurobiology of these two nosologic entities to review their historical and conceptual interplay, highlighting the clinical, genetic and neuroimaging features that may be shared by both disorders or unique to each of them.

Brain atrophy patterns in patients with frontotemporal dementia: voxel-based morphometry

Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by language and behaviour deficits, which is considered the second most common cause of early-onset dementia. Detection of brain atrophy patterns is important for FTD diagnosis. However, the visual assessment of magnetic resonance imaging data may not be sensitive enough requiring the use of objective gray matter (GM) volume determination method. The study was aimed to assess the GM atrophy pattern in patients with FTD compared to control group patients using voxel-based morphometry (VBM). The study included 16 patients with FTD (12 patients with nonfluent agrammatic variant primary progressive aphasia (nfvPPA), three patients with behavioral variant of FTD, and one patient with logopenic variant PPA) and 10 healthy volunteers. VBM of patients with FTD and healthy controls revealed three significant (pFWE-corr < 0.05) atrophy areas in the left inferior frontal, left fusiform, and left supramarginal gyri. Taking into account the predominance of patients with nfvPPA in the group of FTD patients, the additional VBM of this group and control group was carried out, which revealed a distinct atrophy pattern: the reduced GM volume was detected in the left inferior frontal and left middle frontal gyri (pFWE-corr < 0.05). The results obtained indicate that regardless of the clinical variant, there is a certain atrophy pattern characteristic of FTD, which involves both frontotemporal areas and parietal lobe. The example of nfvPPA shows that each variant of the disease is associated with distinct localization of atrophy.

Neuroimaging in genetic frontotemporal dementia and amyotrophic lateral sclerosis

Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) have a strong clinical, genetic and pathological overlap. This review focuses on the current understanding of structural, functional and molecular neuroimaging signatures of genetic FTD and ALS. We overview quantitative neuroimaging studies on the most common genes associated with FTD (MAPT, GRN), ALS (SOD1), and both (C9orf72), and summarize visual observations of images reported in the rarer genes (CHMP2B, TARDBP, FUS, OPTN, VCP, UBQLN2, SQSTM1, TREM2, CHCHD10, TBK1).

Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders

The behavioural variant of frontotemporal dementia (bvFTD) is a frequent cause of early-onset dementia. The diagnosis of bvFTD remains challenging because of the limited accuracy of neuroimaging in the early disease stages and the absence of molecular biomarkers, and therefore relies predominantly on clinical assessment. BvFTD shows significant symptomatic overlap with non-degenerative primary psychiatric disorders including major depressive disorder, bipolar disorder, schizophrenia, obsessive-compulsive disorder, autism spectrum disorders and even personality disorders. To date, ∼50% of patients with bvFTD receive a prior psychiatric diagnosis, and average diagnostic delay is up to 5-6 years from symptom onset. It is also not uncommon for patients with primary psychiatric disorders to be wrongly diagnosed with bvFTD. The Neuropsychiatric International Consortium for Frontotemporal Dementia was recently established to determine the current best clinical practice and set up an international collaboration to share a common dataset for future research. The goal of the present paper was to review the existing literature on the diagnosis of bvFTD and its differential diagnosis with primary psychiatric disorders to provide consensus recommendations on the clinical assessment. A systematic literature search with a narrative review was performed to determine all bvFTD-related diagnostic evidence for the following topics: bvFTD history taking, psychiatric assessment, clinical scales, physical and neurological examination, bedside cognitive tests, neuropsychological assessment, social cognition, structural neuroimaging, functional neuroimaging, CSF and genetic testing. For each topic, responsible team members proposed a set of minimal requirements, optimal clinical recommendations, and tools requiring further research or those that should be developed. Recommendations were listed if they reached a ≥ 85% expert consensus based on an online survey among all consortium participants. New recommendations include performing at least one formal social cognition test in the standard neuropsychological battery for bvFTD. We emphasize the importance of 3D-T1 brain MRI with a standardized review protocol including validated visual atrophy rating scales, and to consider volumetric analyses if available. We clarify the role of 18F-fluorodeoxyglucose PET for the exclusion of bvFTD when normal, whereas non-specific regional metabolism abnormalities should not be over-interpreted in the case of a psychiatric differential diagnosis. We highlight the potential role of serum or CSF neurofilament light chain to differentiate bvFTD from primary psychiatric disorders. Finally, based on the increasing literature and clinical experience, the consortium determined that screening for C9orf72 mutation should be performed in all possible/probable bvFTD cases or suspected cases with strong psychiatric features.

Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement

PURPOSE

Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor prognosis and a substantial reduction in quality of life. The rate of misdiagnosis of FTD is very high, with patients often waiting for years without a firm diagnosis. This study investigates the current state of the misdiagnosis of FTD using a novel artificial intelligence-based algorithm.

PATIENTS & METHODS

An artificial intelligence algorithm has been developed to retrospectively analyse the patient journeys of 47 individuals diagnosed with FTD (age range 52-80). The algorithm analysed the efficiency of patient pathways by utilizing a reward signal of ‒1 to +1 to assess the symptoms, imaging techniques, and clinical judgement in both behavioural and language variants of the disease.

RESULTS

On average, every patient was subjected to 4.93 investigations, of which 67.4% were radiological scans. From first presentation it took on average 939 days for a firm diagnosis. The mean time between appointments was 204 days, and the average patient had their diagnosis altered 7.37 times during their journey. The algorithm proposed improvements by evaluating the interventions that resulted in a decreased reward signal to both the individual and the population as a whole.

CONCLUSIONS

The study proves that the algorithm can efficiently guide clinical practice and improve the accuracy of the diagnosis of FTD whilst making the process of auditing faster and more economically viable.

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.

FTD spectrum: Neuroimaging across the FTD spectrum

Frontotemporal dementia is a complex and heterogeneous neurodegenerative disease that encompasses many clinical syndromes, pathological diseases, and genetic mutations. Neuroimaging has played a critical role in our understanding of the underlying pathophysiology of frontotemporal dementia and provided biomarkers to aid diagnosis. Early studies defined patterns of neurodegeneration and hypometabolism associated with the clinical, pathological and genetic aspects of frontotemporal dementia, with more recent studies highlighting how the breakdown of structural and functional brain networks define frontotemporal dementia. Molecular positron emission tomography ligands allowing the in vivo imaging of tau proteins have also provided important insights, although more work is needed to understand the biology of the currently available ligands.

Individual Prediction of Behavioral Variant Frontotemporal Dementia Development Using Multivariate Pattern Analysis of Magnetic Resonance Imaging Data

BACKGROUND

Patients with behavioral variant of frontotemporal dementia (bvFTD) initially may only show behavioral and/or cognitive symptoms that overlap with other neurological and psychiatric disorders. The diagnostic accuracy is dependent on progressive symptoms worsening and frontotemporal abnormalities on neuroimaging findings. Predictive biomarkers could facilitate the early detection of bvFTD.

OBJECTIVE

To determine the prognostic accuracy of clinical and structural MRI data using a support vector machine (SVM) classification to predict the 2-year clinical follow-up diagnosis in a group of patients presenting late-onset behavioral changes.

METHODS

Data from 73 patients were included and divided into probable/definite bvFTD (n = 18), neurological (n = 28), and psychiatric (n = 27) groups based on 2-year follow-up diagnosis. Grey-matter volumes were extracted from baseline structural MRI scans. SVM classifiers were used to perform three binary classifications: bvFTD versus neurological and psychiatric, bvFTD versus neurological, and bvFTD versus psychiatric group(s), and one multi-class classification. Classification performance was determined for clinical and neuroimaging data separately and their combination using 5-fold cross-validation.

RESULTS

Accuracy of the binary classification tasks ranged from 72-82% (p < 0.001) with adequate sensitivity (67-79%), specificity (77-88%), and area-under-the-receiver-operator-curve (0.80-0.9). Multi-class accuracy ranged between 55-59% (p < 0.001). The combination of clinical and voxel-wise whole brain data showed the best performance overall.

CONCLUSION

These results show the potential for automated early confirmation of diagnosis for bvFTD using machine learning analysis of clinical and neuroimaging data in a diverse and clinically relevant sample of patients.

Dual-phase [18F]florbetapir in frontotemporal dementia

PURPOSE

The PET tracer [18F]florbetapir is a specific fibrillar amyloid-beta (Aβ) biomarker. During the late scan phase (> 40 min), it provides pathological information about Aβ status. Early scan phase (0-10 min) can provide FDG-'like' information. The current investigation tested the feasibility of using florbetapir as a dual-phase biomarker in behavioural variant frontotemporal dementia (bvFTD).

METHODS

Eight bvFTD patients underwent [18F]florbetapir and [18]FDG-PET scans. Additionally, ten healthy controls and ten AD patients underwent florbetapir-PET only. PET data were acquired dynamically for 60-min post-injection. The bvFTD PET data were used to define an optimal time window, representing blood flow-related pseudo-metabolism ('pseudo-FDG'), of florbetapir data that maximally correlated with the corresponding real FDG SUVR (40-60 min) in a composite neocortical FTD region.

RESULTS

A 2 to 5-min time window post-injection of the florbetapir-PET data provided the largest correlation (Pearson's r = 0.79, p = 0.02) to the FDG data. The pseudo-FDG images demonstrated strong internal consistency with actual FDG data and were also visually consistent with the bvFTD patients' hypometabolic profiles. The ability to identify bvFTD from blind visual rating of pseudo-FDG images was consistent with previous reports using FDG data (sensitivity = 75%, specificity = 85%).

CONCLUSIONS

This investigation demonstrates that early phase florbetapir uptake shows a reduction of frontal lobe perfusion in bvFTD, similar to metabolic findings with FDG. Thus, dynamic florbetapir scans can serve as a dual-phase biomarker in dementia patients to distinguish FTD from AD and cognitively normal elderly, removing the need for a separate FDG-PET scan in challenging dementia cases.

Frontotemporal Dementia Misdiagnosed for Post-Treatment Lyme Disease Syndrome or vice versa? A Treviso Dementia (TREDEM) Registry Case Report

We describe the case of a 61-year-old woman diagnosed with Borreliosis at the age of 57. Subsequently, the patient developed depression, anxiety, and behavioral disturbances. A lumbar puncture excluded the condition of Neuroborreliosis. The diagnostic workup included: an MRI scan, a 18F-FDG PET, a 123I-ioflupane-SPECT, an amyloid-β PET, a specific genetic analysis, and a neuropsychological evaluation. Based on our investigation, the patient was diagnosed with probable behavioral-frontotemporal dementia (bvFTD), whereas in the previous years, the patient had been considered firstly as a case of Post-Treatment-Lyme Disease and, secondly, a psychiatric patient. We believe that, in the present case, such initial symptoms of Borrelia infection may have superimposed on those of bvFTD rather than playing as a contributory cause.

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.

Amsterdam Dementia Cohort: Performing Research to Optimize Care

The Alzheimer center of the VU University Medical Center opened in 2000 and was initiated to combine both patient care and research. Together, to date, all patients forming the Amsterdam Dementia Cohort number almost 6,000 individuals. In this cohort profile, we provide an overview of the results produced based on the Amsterdam Dementia Cohort. We describe the main results over the years in each of these research lines: 1) early diagnosis, 2) heterogeneity, and 3) vascular factors. Among the most important research efforts that have also impacted patients' lives and/or the research field, we count the development of novel, easy to use diagnostic measures such as visual rating scales for MRI and the Amsterdam IADL Questionnaire, insight in different subgroups of AD, and findings on incidence and clinical sequelae of microbleeds. Finally, we describe in the outlook how our research endeavors have improved the lives of our patients.

Neuroimaging in Dementia

Although the diagnosis of dementia still is primarily based on clinical criteria, neuroimaging is playing an increasingly important role. This is in large part due to advances in techniques that can assist with discriminating between different syndromes. Magnetic resonance imaging remains at the core of differential diagnosis, with specific patterns of cortical and subcortical changes having diagnostic significance. Recent developments in molecular PET imaging techniques have opened the door for not only antemortem but early, even preclinical, diagnosis of underlying pathology. This is vital, as treatment trials are underway for pharmacological agents with specific molecular targets, and numerous failed trials suggest that earlier treatment is needed. This article provides an overview of classic neuroimaging findings as well as new and cutting-edge research techniques that assist with clinical diagnosis of a range of dementia syndromes, with an emphasis on studies using pathologically proven cases.

Imaging and fluid biomarkers in frontotemporal dementia

Frontotemporal dementia (FTD), the second most common type of presenile dementia, is a heterogeneous neurodegenerative disease characterized by progressive behavioural and/or language problems, and includes a range of clinical, genetic and pathological subtypes. The diagnostic process is hampered by this heterogeneity, and correct diagnosis is becoming increasingly important to enable future clinical trials of disease-modifying treatments. Reliable biomarkers will enable us to better discriminate between FTD and other forms of dementia and to predict disease progression in the clinical setting. Given that different underlying pathologies probably require specific pharmacological interventions, robust biomarkers are essential for the selection of patients with specific FTD subtypes. This Review emphasizes the increasing availability and potential applications of structural and functional imaging biomarkers, and cerebrospinal fluid and blood fluid biomarkers in sporadic and genetic FTD. The relevance of new MRI modalities - such as voxel-based morphometry, diffusion tensor imaging and arterial spin labelling - in the early stages of FTD is discussed, together with the ability of these modalities to classify FTD subtypes. We highlight promising new fluid biomarkers for staging and monitoring of FTD, and underline the importance of large, multicentre studies of individuals with presymptomatic FTD. Harmonization in the collection and analysis of data across different centres is crucial for the implementation of new biomarkers in clinical practice, and will become a great challenge in the next few years.

Cerebrospinal fluid biomarker examination as a tool to discriminate behavioral variant frontotemporal dementia from primary psychiatric disorders

INTRODUCTION

To prospectively determine the diagnostic value of cerebrospinal fluid (CSF) levels total-tau (tau) to amyloid-β_1-42 ratio (Aβ_1-42) ratio (tau/Aβ_1-42 ratio), phosphorylated-tau (p-tau) to tau ratio (p-tau/tau ratio), neurofilament light chain (NfL) and YKL40 in the late-onset frontal lobe syndrome, in particular for the differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) versus primary psychiatric disorders (PSY).

METHOD

We included patients with a multidisciplinary 2-year-follow-up diagnosis of probable/definite bvFTD (n = 22) or PSY (n = 25), who underwent a detailed neuropsychiatric clinical examination, neuropsychological test battery, and magnetic resonance imaging at baseline. In all cases, CSF was collected through lumbar puncture at baseline. We compared CSF biomarker levels between the two groups and measured the diagnostic accuracy for probable/definite bvFTD, using the follow-up diagnosis as the reference standard.

RESULTS

The best discriminators between probable/definite bvFTD and PSY were the levels of CSF NfL (area under the curve [AUC] 0.93, P < .001, 95% confidence interval [CI] 0.85-1.00), p-tau/tau ratio (AUC 0.87, P < .001, 95% CI 0.77-0.97), and YKL40 (AUC 0.82, P = .001, 95% CI 0.68-0.97). The combination of these three biomarkers had a sensitivity of 91% (95% CI 66%-100%) at a specificity of 83% (95% CI 65%-95%) with an AUC of 0.94 (P < .001, 95% CI 0.87-1.00) for bvFTD. CSF tau/Aβ_1-42 ratio was less accurate in differentiating between bvFTD and PSY.

DISCUSSION

We found a good diagnostic accuracy for higher levels of CSF NfL and YKL40 and reduced p-tau/tau ratio in distinguishing bvFTD from PSY. We advocate the use of these CSF biomarkers as potential additional tools to neuroimaging in the diagnosis of bvFTD versus PSY.