A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia

Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review

PURPOSE

Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings.

METHODS

Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3 years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy.

RESULTS

Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP.

CONCLUSION

Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.

The metabolic spatial covariance pattern of definite idiopathic normal pressure hydrocephalus: an FDG PET study with principal components analysis

Identification of patients with idiopathic normal pressure hydrocephalus (iNPH) in a collective with suspected neurodegenerative disease is essential. This study aimed to determine the metabolic spatial covariance pattern of iNPH on FDG PET using an established technique based on scaled subprofile model principal components analysis (SSM-PCA).We identified 11 patients with definite iNPH. By applying SSM-PCA to the FDG PET data, they were compared to 48 age-matched healthy controls to determine the whole-brain voxel-wise metabolic spatial covariance pattern of definite iNPH (iNPH-related pattern, iNPHRP). The iNPHRP score was compared between groups of patients with definite iNPH, possible iNPH (N = 34), Alzheimer's (AD, N = 38), and Parkinson's disease (PD, N = 35) applying pairwise Mann-Whitney U tests and correction for multiple comparisons.SSM-PCA of FDG PET revealed an iNPHRP that is characterized by relative negative voxel weights at the vicinity of the lateral ventricles and relative positive weights in the paracentral midline region. The iNPHRP scores of patients with definite iNPH were substantially higher than in patients with AD and PD (both p < 0.05) and non-significantly higher than those of patients with possible iNPH. Subject scores of the iNPHRP discriminated definite iNPH from AD and PD with 96% and 100% accuracy and possible iNPH from AD and PD with 83% and 86% accuracy.We defined a novel metabolic spatial covariance pattern of iNPH that might facilitate the differential diagnosis of iNPH versus other neurodegenerative disorders. The knowledge of iNPH-associated alterations in the cerebral glucose metabolism is of high relevance as iNPH constitutes an important differential diagnosis to dementia and movement disorders.

Regional Glymphatic Abnormality in Behavioral Variant Frontotemporal Dementia

OBJECTIVES

Glymphatic function has not yet been explored in behavioral variant frontotemporal dementia (bvFTD). The spatial correlation between regional glymphatic function and bvFTD remains unknown.

METHOD

A total of 74 patients with bvFTD and 67 age- and sex-matched healthy controls (HCs) were selected from discovery dataset and replication dataset. All participants underwent neuropsychological assessment. Glymphatic measures including choroid plexus (CP) volume, diffusion tensor imaging along the perivascular (DTI-ALPS) index, and coupling between blood-oxygen-level-dependent signals and cerebrospinal fluid signals (BOLD-CSF coupling), were compared between the two groups. Regional glymphatic function was evaluated by dividing DTI-ALPS and BOLD-CSF coupling into anterior, middle, and posterior regions. The bvFTD-related metabolic pattern was identified using spatial covariance analysis based on l8 F-FDG-PET.

RESULTS

Patients with bvFTD showed higher CP volume (p < 0.001); anterior and middle DTI-ALPS (p < 0.001); and weaker anterior BOLD-CSF coupling (p < 0.05) than HCs after controlling for cortical gray matter volume in both datasets. In bvFTD from the discovery dataset, the anterior DTI-ALPS was negatively associated with the expression of the bvFTD-related metabolic pattern (r = -0.52, p = 0.034) and positively related with regional standardized uptake value ratios of l8 F-FDG-PET in bvFTD-related brain regions (r range: 0.49 to 0.62, p range: 0.017 to 0.047). Anterior and middle glymphatic functions were related to global cognition and disease severity.

INTERPRETATION

Our findings reveal abnormal glymphatic function, especially in the anterior and middle regions of brain in bvFTD. Regional glymphatic dysfunction may contribute to the pathogenesis of bvFTD. ANN NEUROL 2023;94:442-456.

Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network

BACKGROUND AND PURPOSE

Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored.

METHODS

We explored 2-[¹⁸ F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods.

RESULTS

The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization.

CONCLUSIONS

The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.

Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia

Behavioral variant of frontotemporal dementia (bvFTD) is common among young-onset dementia patients. While bvFTD-specific multivariate metabolic brain pattern (bFDRP) has been identified previously, little is known about its temporal evolution, internal structure, effect of atrophy, and its relationship with nonspecific resting-state networks such as default mode network (DMN). In this multicenter study, we explored FDG-PET brain scans of 111 bvFTD, 26 Alzheimer's disease, 16 Creutzfeldt-Jakob's disease, 24 semantic variant primary progressive aphasia (PPA), 18 nonfluent variant PPA and 77 healthy control subjects (HC) from Slovenia, USA, and Germany. bFDRP was identified in a cohort of 20 bvFTD patients and age-matched HC using scaled subprofile model/principle component analysis and validated in three independent cohorts. It was characterized by hypometabolism in frontal cortex, insula, anterior/middle cingulate, caudate, thalamus, and temporal poles. Its expression in bvFTD patients was significantly higher compared to HC and other dementia syndromes (p < .0004), correlated with cognitive decline (p = .0001), and increased over time in longitudinal cohort (p = .0007). Analysis of internal network organization by graph-theory methods revealed prominent network disruption in bvFTD patients. We have further found a specific atrophy-related pattern grossly corresponding to bFDRP; however, its contribution to the metabolic pattern was minimal. Finally, despite the overlap between bFDRP and FDG-PET-derived DMN, we demonstrated a predominant role of the specific bFDRP. Taken together, we validated the bFDRP network as a diagnostic/prognostic biomarker specific for bvFTD, provided a unique insight into its highly reproducible internal structure, and proved that bFDRP is unaffected by structural atrophy and independent of normal resting state networks loss.

Automated differential diagnosis of dementia syndromes using FDG PET and machine learning

BACKGROUND

Metabolic brain imaging with 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes.

METHODS

We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer's disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients' clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer's disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multi-class support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations.

RESULTS

Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC.

CONCLUSION

Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.

Identification and validation of Alzheimer's disease-related metabolic brain pattern in biomarker confirmed Alzheimer's dementia patients

Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer's disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer's disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients' clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76-0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.

The roles of executive functioning, simple attention, and medial temporal lobes in early learning, late learning, and delayed recall

Fractionating performance of a verbal list-learning test can provide a nuanced interpretation of the relationship between brain networks and learning and memory abilities. Within older adult samples, including those with mild cognitive impairment and Alzheimer’s disease, cortical volumes for attention and executive functioning networks correlate more strongly with neuropsychological performance measures of early learning trials relative to late learning and delayed recall. In contrast, medial temporal lobe (MTL) structures, such as the hippocampus, are more strongly correlated to performance on late learning and delayed recall measures relative to early learning. We sought to extend these findings by evaluating the contributions of simple attention, executive function (EF), and MTL structures to learning and recall in a cognitively heterogeneous sample of older adults that included healthy controls (n = 54), adults with MCI (n = 63), and those with dementia (n = 13). We used canonical correlation analyses to test the hypotheses that the contributions of EF, simple attention, and the MTL to verbal memory would differ across phases of learning and recall. Results showed that relationships between the MTL and memory were the only ones to demonstrate a graded pattern of association, ranging from r = .46 to .57 across early learning, late learning, and delayed recall. Simple attention and EF were both significantly and moderately related to learning and recall, but those relationships did not vary across phases as hypothesized. We explore alternative interpretations for our discrepant findings, including the influence of sample characteristics and methodology, advocating for multivariate approaches.

A disease-specific metabolic imaging marker for diagnosis and progression evaluation of semantic variant primary progressive aphasia

BACKGROUND AND PURPOSE

The diagnosis and monitoring of semantic variant primary progressive aphasia (sv-PPA) are clinically challenging. We aimed to establish a distinctive metabolic pattern in sv-PPA for diagnosis and severity evaluation.

METHODS

Fifteen sv-PPA patients and 15 controls were enrolled to identify sv-PPA-related pattern (sv-PPARP) by principal component analysis of ¹⁸ F-fluorodeoxyglucose positron emission tomography. Eighteen Alzheimer disease dementia (AD) and 14 behavioral variant frontotemporal dementia (bv-FTD) patients were enrolled to test the discriminatory power. Correspondingly, regional metabolic activities extracted from the voxelwise analysis were evaluated for the discriminatory power.

RESULTS

The sv-PPARP was characterized as decreased metabolic activity mainly in the bilateral temporal lobe (left predominance), middle orbitofrontal gyrus, left hippocampus/parahippocampus gyrus, fusiform gyrus, insula, inferior orbitofrontal gyrus, and striatum, with increased activity in the bilateral lingual gyrus, cuneus, calcarine gyrus, and right precentral and postcentral gyrus. The pattern expression had significant discriminatory power (area under the curve [AUC] = 0.98, sensitivity = 100%, specificity = 94.4%) in distinguishing sv-PPA from AD, and the asymmetry index offered complementary discriminatory power (AUC = 0.91, sensitivity = 86.7%, specificity = 92.9%) in distinguishing sv-PPA from bv-FTD. In sv-PPA patients, the pattern expression correlated with Boston Naming Test scores at baseline and showed significant increase in the subset of patients with follow-up. The voxelwise analysis showed similar topography, and the regional metabolic activities had equivalent or better discriminatory power and clinical correlations with Boston Naming Test scores. The ability to reflect disease progression in longitudinal follow-up seemed to be inferior to the pattern expression.

CONCLUSIONS

The sv-PPARP might serve as an objective biomarker for diagnosis and progression evaluation.

Brain SPECT as a Biomarker of Neurodegeneration in Dementia in the Era of Molecular Imaging: Still a Valid Option?

Biomarkers are playing a progressively leading role in both clinical practice and scientific research in dementia. Although amyloid and tau biomarkers have gained ground in the clinical community in recent years, neurodegeneration biomarkers continue to play a key role due to their ability to identify different patterns of brain involvement that sign the transition between asymptomatic and symptomatic stages of the disease with high sensitivity and specificity. Both ¹⁸F-FDG positron emission tomography (PET) and perfusion single photon emission computed tomography (SPECT) have proved useful to reveal the functional alterations underlying various neurodegenerative diseases. Although the focus of nuclear neuroimaging has shifted to PET, the lower cost and wider availability of SPECT make it a still valid alternative for the study of patients with dementia. This review discusses the principles of both techniques, compares their diagnostic performance for the diagnosis of neurodegenerative diseases and highlights the role of SPECT to characterize patients from low- and middle-income countries, where special care of additional costs is particularly needed to meet the new recommendations for the diagnosis and characterization of patients with dementia.

Neuroimaging evaluation of deep brain stimulation in the treatment of representative neurodegenerative and neuropsychiatric disorders

Brain stimulation technology has become a viable modality of reversible interventions in the effective treatment of many neurological and psychiatric disorders. It is aimed to restore brain dysfunction by the targeted delivery of specific electronic signal within or outside the brain to modulate neural activity on local and circuit levels. Development of therapeutic approaches with brain stimulation goes in tandem with the use of neuroimaging methodology in every step of the way. Indeed, multimodality neuroimaging tools have played important roles in target identification, neurosurgical planning, placement of stimulators and post-operative confirmation. They have also been indispensable in pre-treatment screen to identify potential responders and in post-treatment to assess the modulation of brain circuitry in relation to clinical outcome measures. Studies in patients to date have elucidated novel neurobiological mechanisms underlying the neuropathogenesis, action of stimulations, brain responses and therapeutic efficacy. In this article, we review some applications of deep brain stimulation for the treatment of several diseases in the field of neurology and psychiatry. We highlight how the synergistic combination of brain stimulation and neuroimaging technology is posed to accelerate the development of symptomatic therapies and bring revolutionary advances in the domain of bioelectronic medicine.

Cognitive reserve hypothesis in frontotemporal dementia: A FDG-PET study

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The cognitive reserve hypothesis is also applicable for FTD.

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The educational level predicts left-temporal hypometabolism in FTD.

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Residualized cognitive performance is positively correlated with education.

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Residualized cognitive performance is negatively correlated with hypometabolism.

Background and objective

Reserve is defined as the ability to maintain cognitive functions relatively well at a given level of pathology. Early life experiences such as education are associated with lower dementia risk in general. However, whether more years of education guards against the impact of brain alterations also in frontotemporal dementia (FTD) has not been shown in a large patient collective. Therefore, we assessed whether education is associated with relatively high cognitive performance despite the presence of [¹⁸F]-fluorodeoxyglucose positron-emission-tomography (FDG-PET) hypometabolism in FTD.

Methods

Sixty-six FTD subjects (age 67 ± 8 years) and twenty-four cognitively healthy controls (HC) were evaluated. Brain regions with FTD-related glucose hypometabolism in the contrast against HC and brain regions that correlate with the cognitive function were defined by a voxel-based analysis and individual FDG-PET values were extracted from all frontotemporal brain areas. Linear regression analysis served to test if education is associated with residualized cognitive performance and regional FDG-PET hypometabolism after controlling for global cognition.

Results

Compared to healthy controls, patients with FTD showed glucose hypometabolism in bilateral frontal and temporal brain areas whereas cognition was only associated with deteriorated glucose metabolism in the left temporal lobe. The education level was significantly correlated with the residualized cognitive performance (residuals from regression analysis between hypometabolism and cognitive function as a quantitative index of reserve) and also negatively correlated with left temporal FDG-PET hypometabolism after controlling for cognition.

Conclusions

In patients with FTD, the education level predicts the existing left temporal FDG-PET hypometabolism at the same cognition level, supporting the cognitive reserve hypothesis in FTD.

Integrity of Neurocognitive Networks in Dementing Disorders as Measured with Simultaneous PET/Functional MRI

Functional MRI (fMRI) studies have reported altered integrity of large-scale neurocognitive networks (NCNs) in dementing disorders. However, findings on the specificity of these alterations in patients with Alzheimer disease (AD) and behavioral-variant frontotemporal dementia (bvFTD) are still limited. Recently, NCNs have been successfully captured using PET with ¹⁸F-FDG. Methods: Network integrity was measured in 72 individuals (38 male) with mild AD or bvFTD, and in healthy controls, using a simultaneous resting-state fMRI and ¹⁸F-FDG PET. Indices of network integrity were calculated for each subject, network, and imaging modality. Results: In either modality, independent-component analysis revealed 4 major NCNs: anterior default-mode network (DMN), posterior DMN, salience network, and right central executive network (CEN). In fMRI data, the integrity of the posterior DMN was found to be significantly reduced in both patient groups relative to controls. In the AD group the anterior DMN and CEN appeared to be additionally affected. In PET data, only the integrity of the posterior DMN in patients with AD was reduced, whereas 3 remaining networks appeared to be affected only in patients with bvFTD. In a logistic regression analysis, the integrity of the anterior DMN as measured with PET alone accurately differentiated between the patient groups. A correlation between indices of 2 imaging modalities was low overall. Conclusion: FMRI and ¹⁸F-FDG PET capture partly different aspects of network integrity. A higher disease specificity for NCNs as derived from PET data supports metabolic connectivity imaging as a promising diagnostic tool.

Crossing Borders Between Frontotemporal Dementia and Psychiatric Disorders: An Updated Overview

Frontotemporal dementia (FTD) includes a group of neurocognitive syndromes, clinically characterized by altered behaviors, impairment of language proficiency, and altered executive functioning. FTD is one of the most frequently observed forms of dementia in the elderly population and the most common in presenile age. As for other subtypes of dementia, FTD incidence is constantly on the rise due to the steadily increasing age of the population, and its recognition is now becoming a determinant for clinicians. FTD and psychiatric disorders can overlap in terms of clinical presentations by sharing a common genetic predisposition and neuropathological mechanism in some cases. Nonetheless, this association is often unclear and underestimated. Since its first reports, research into FTD has constantly grown, with the identification of recent findings related to its neuropathology, genetic, clinical, and therapeutic issues. Literature is thriving on this topic, with numerous research articles published in recent years. In the present review, we aimed to provide an updated description of the clinical manifestations that link and potentially confound the diagnosis of FTD and psychiatric disorders in order to improve their differential diagnosis and early detection. In particular, we systematically reviewed the literature, considering articles specifically focused on the behavioral variant FTD, published after 2015 on the PubMed database.

Update on PET in neurodegenerative and neuroinflammatory disorders manifesting on a behavioural level: imaging for differential diagnosis

PURPOSE OF REVIEW

To give an update on recent findings concerning the use of PET for differential diagnosis in neurodegenerative and neuroinflammatory disorders manifesting on a behavioural level.

RECENT FINDINGS

Although accurate differential diagnosis of dementia can be achieved by imaging disease-specific patterns of cerebral glucose metabolism with [F]fluorodeoxyglucose ([F]FDG)-PET, the diagnostic impact of [F]FDG-PET in primary psychiatric disorders is limited. Amyloid-beta PET provides an incremental value beyond [F]FDG-PET in the differential diagnosis of dementia and was proposed as a biomarker defining the so-called Alzheimer continuum. Recently developed tau-specific tracers might also aid in the diagnostic process (biological definition of Alzheimer's disease together with amyloid-beta). Surpassing the diagnostic accuracy of other techniques, such as MRI, [F]FDG-PET has also gained widespread clinical use for diagnosis and follow-up of paraneoplastic and autoimmune disorders of the central nervous system (CNS) as an important differential diagnosis for rapid progressive dementia and subacute onset of psychiatric syndromes.

SUMMARY

Molecular neuroimaging with PET is an established method for the differential diagnosis of neurodegenerative and autoimmune CNS disorders manifesting on a behavioural level with significant therapeutic and prognostic impact. Future prospective studies are needed to define the value of tau imaging for diagnosis and prognosis in neurodegenerative disorders.

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.