Neuroimaging in Frontotemporal Dementia: Heterogeneity and Relationships with Underlying Neuropathology

A comparison of visual assessment and semi-quantification for the diagnostic and prognostic use of [18F]flortaucipir PET in a memory clinic cohort

PURPOSE

[18F]Flortaucipir PET is a powerful diagnostic and prognostic tool for Alzheimer's disease (AD). Tau status definition is mainly based in the literature on semi-quantitative measures while in clinical settings visual assessment is usually preferred. We compared visual assessment with established semi-quantitative measures to classify subjects and predict the risk of cognitive decline in a memory clinic population.

METHODS

We included 245 individuals from the Geneva Memory Clinic who underwent [18F]flortaucipir PET. Amyloid status was available for 207 individuals and clinical follow-up for 135. All scans were blindly evaluated by three independent raters who visually classified the scans according to Braak stages. Standardized uptake value ratio (SUVR) values were obtained from a global meta-ROI to define tau positivity, and the Simplified Temporo-Occipital Classification (STOC) was applied to obtain semi-quantitatively tau stages. The agreement between measures was tested using Cohen's kappa (k). ROC analysis and linear mixed-effects models were applied to test the diagnostic and prognostic values of tau status and stages obtained with the visual and semi-quantitative approaches.

RESULTS

We found good inter-rater reliability in the visual interpretation of tau Braak stages, independently from the rater's expertise (k>0.68, p<0.01). A good agreement was equally found between visual and SUVR-based classifications for tau status (k=0.67, p<0.01). All tau-assessment modalities significantly discriminated amyloid-positive MCI and demented subjects from others (AUC>0.80) and amyloid-positive from negative subjects (AUC>0.85). Linear mixed-effect models showed that tau-positive individuals presented a significantly faster cognitive decline than the tau-negative group (p<0.01), independently from the classification method.

CONCLUSION

Our results show that visual assessment is reliable for defining tau status and stages in a memory clinic population. The high inter-rater reliability, the substantial agreement, and the similar diagnostic and prognostic performance of visual rating and semi-quantitative methods demonstrate that [18F]flortaucipir PET can be robustly assessed visually in clinical practice.

Amyloid-induced neurodegeneration: A comprehensive review through aggregomics perception of proteins in health and pathology

Amyloidosis of protein caused by fibrillation and aggregation are some of the most exciting new edges not only in protein sciences but also in molecular medicines. The present review discusses recent advancements in the field of neurodegenerative diseases and therapeutic applications with ongoing clinical trials, featuring new areas of protein misfolding resulting in aggregation. The endogenous accretion of protein fibrils having fibrillar morphology symbolizes the beginning of neuro-disorders. Prognostic amyloidosis is prominent in numerous degenerative infections such as Alzheimer's and Parkinson's disease, Amyotrophic lateral sclerosis (ALS), etc. However, the molecular basis determining the intracellular or extracellular evidence of aggregates, playing a significant role as a causative factor in neurodegeneration is still unclear. Structural conversions and protein self-assembly resulting in the formation of amyloid oligomers and fibrils are important events in the pathophysiology of the disease. This comprehensive review sheds light on the evolving landscape of potential treatment modalities, highlighting the ongoing clinical trials and the potential socio-economic impact of novel therapeutic interventions in the realm of neurodegenerative diseases. Furthermore, many drugs are undergoing different levels of clinical trials that would certainly help in treating these disorders and will surely improve the socio-impact of human life.

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.

Bridging the gap between scientific research of rare diseases and the affected patients and families

In 2015, my father was diagnosed with primary progressive aphasia (PPA), a type of frontotemporal dementia that my family and I knew nothing about. Medical professionals told us that there was no research on the disease, and I believed this until very recently. I took a class in neurobiology, leading me to attempt to document this lack of research and create a call to action for the research and treatment of rare disorders. However, I was met with an overwhelming amount of information regarding PPA that I was not expecting to find. I was frustrated that I was not given this information; moreover, I did not understand why it was all being 'hidden' from me. After discussion with my mother, I realized that my science education allowed me to find and interpret this information, but more importantly, that not everyone has this same privilege. My call to action pivoted into a call for better communication and for open access to biomedical information. Regardless of the existence and quality of literature about rare diseases, most of the information is out of reach of the public.The public often does not have the scientific literacy to understand the complexities of the genre that is required for comprehension. I recognize that not every patient and family may wish to access the information generated by biomedical research. I argue that they have a right to examine these findings because they are the ones that are being the most deeply affected by these disorders. While the translation of information may seem cumbersome , the impact it could have on patients, caregivers, and providers is worth the effort.

Neurite-based white matter alterations in MAPT mutation carriers: A multi-shell diffusion MRI study in the ALLFTD consortium

We assessed white matter (WM) integrity in MAPT mutation carriers (16 asymptomatic, 5 symptomatic) compared to 31 non-carrier family controls using diffusion tensor imaging (DTI) (fractional anisotropy; FA, mean diffusivity; MD) and neurite orientation dispersion and density imaging (NODDI) (neurite density index; NDI, orientation and dispersion index; ODI). Linear mixed-effects models accounting for age and family relatedness revealed alterations across DTI and NODDI metrics in all mutation carriers and in symptomatic carriers, with the most significant differences involving fronto-temporal WM tracts. Asymptomatic carriers showed higher entorhinal MD and lower cingulum FA and patterns of higher ODI mostly involving temporal areas and long association and projections fibers. Regression models between estimated time to or time from disease and DTI and NODDI metrics in key regions (amygdala, cingulum, entorhinal, inferior temporal, uncinate fasciculus) in all carriers showed increasing abnormalities with estimated time to or time from disease onset, with FA and NDI showing the strongest relationships. Neurite-based metrics, particularly ODI, appear to be particularly sensitive to early WM involvement in asymptomatic carriers.

Behavioral variant frontotemporal dementia associated with GRN and ErbB4 gene mutations: a case report and literature review

OBJECTIVE

To report the clinical manifestation and genetic characteristics of a patient having frontotemporal dementia (FTD) with abnormal behavior and unstable walking.

METHODS

The clinical and imaging features of a patient who was eventually diagnosed with FTD were analyzed. The patient's neuropsychological, PET-CT, electromyography, and genetic data were collected. Furthermore, the patient's blood samples were examined for FTD-related genes.

RESULTS

The patient was a 52-year-old man with hidden onset. The symptoms progressed gradually, presenting with abnormal behaviors, including repeated shopping, taking away other people's things, constantly eating snacks, and frequently calling friends at night. The patient also exhibited executive dysfunction, such as the inability to cook and multiple driving problems, e.g., constantly deviates from his lane while driving. In addition, the patient showed personality changes such as irritability, indifference, and withdrawal, as well as motor symptoms, including unstable walking and frequent falls when walking. Brain magnetic resonance imaging revealed hippocampal sclerosis along with widening and deepening of the bilateral temporal lobe sulcus. Brain metabolic imaging via PET-CT demonstrated decreased metabolism in the bilateral prefrontal lobe, with the abnormal energy metabolism indicating FTD. Lastly, blood sample analysis detected mutations in the amyotrophic lateral sclerosis (ALS)-related GRN gene c.1352C > T (p.P451L) and ErbB4 gene c.256 T > C (p.Y86H).

CONCLUSION

This is the first case of heterozygous mutations in the GRN and ErbB4 genes in FTD alone. The GRN and ErbB4 genes are likely to be important in the pathogenesis of FTD, expanding the common genetic profile of ALS and FTD.

Emerging perspectives of synaptic biomarkers in ALS and FTD

Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are debilitating neurodegenerative diseases with shared pathological features like transactive response DNA-binding protein of 43 kDa (TDP-43) inclusions and genetic mutations. Both diseases involve synaptic dysfunction, contributing to their clinical features. Synaptic biomarkers, representing proteins associated with synaptic function or structure, offer insights into disease mechanisms, progression, and treatment responses. These biomarkers can detect disease early, track its progression, and evaluate therapeutic efficacy. ALS is characterized by elevated neurofilament light chain (NfL) levels in cerebrospinal fluid (CSF) and blood, correlating with disease progression. TDP-43 is another key ALS biomarker, its mislocalization linked to synaptic dysfunction. In FTD, TDP-43 and tau proteins are studied as biomarkers. Synaptic biomarkers like neuronal pentraxins (NPs), including neuronal pentraxin 2 (NPTX2), and neuronal pentraxin receptor (NPTXR), offer insights into FTD pathology and cognitive decline. Advanced technologies, like machine learning (ML) and artificial intelligence (AI), aid biomarker discovery and drug development. Challenges in this research include technological limitations in detection, variability across patients, and translating findings from animal models. ML/AI can accelerate discovery by analyzing complex data and predicting disease outcomes. Synaptic biomarkers offer early disease detection, personalized treatment strategies, and insights into disease mechanisms. While challenges persist, technological advancements and interdisciplinary efforts promise to revolutionize the understanding and management of ALS and FTD. This review will explore the present comprehension of synaptic biomarkers in ALS and FTD and discuss their significance and emphasize the prospects and obstacles.

Targeted examination of amyloid beta and tau protein accumulation via positron emission tomography for the differential diagnosis of Alzheimer's disease based on the A/T(N) research framework

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases among the older population. Its main pathological features include the abnormal deposition of extracellular amyloid-β plaques and the intracellular neurofibrillary tangles of tau proteins. Its clinical presentation is complex. This review introduces the pathological processes in AD and other common neurodegenerative diseases. It then discusses the positron emission tomography (PET) probes that target amyloid-β plaques and tau proteins for diagnosing AD. According to the A/T(N) research framework, combined targeted amyloid-β and tau protein detection via PET to further improve the diagnostic accuracy of AD. In particular, the properties of the 18F-flortaucipir and 18F-MK6240 tracers-may be more beneficial in helping to differentiate AD from other common neurodegenerative diseases, such as dementia with Lewy bodies, Parkinson's disease dementia, and frontotemporal dementia. Furthermore, the A/T(N) research framework should be used as the clinical diagnosis model of AD in the future.

Assessing network degeneration and phenotypic heterogeneity in genetic frontotemporal lobar degeneration by decoding FDG-PET

Genetic mutations causative of frontotemporal lobar degeneration (FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter-individual variability in their patterns of network degeneration and clinical manifestations. We collected clinical and 18Fluorodeoxyglucose-positron emission tomography (FDG-PET) data from 39 patients with genetic FTLD, including 11 carrying the C9orf72 hexanucleotide expansion, 16 carrying a MAPT mutation and 12 carrying a GRN mutation. We performed a spectral covariance decomposition analysis between FDG-PET images to yield unbiased latent patterns reflective of whole brain patterns of metabolism ("eigenbrains" or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB-based predictions of genetic mutation and predominant clinical phenotype (i.e., behavior/personality, language, asymptomatic). Five EBs were significant and explained 58.52 % of the covariance between FDG-PET images. EBs indicative of hypometabolism in left frontotemporal and temporo-parietal areas distinguished GRN mutation carriers from other genetic mutations and were associated with predominant language phenotypes. EBs indicative of hypometabolism in prefrontal and temporopolar areas with a right hemispheric predominance were mostly associated with predominant behavioral phenotypes and distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 79.5 % and 76.9 % in predicting genetic status and predominant clinical phenotype, respectively. A small number of EBs explained a high proportion of covariance in patterns of network degeneration across FTLD-related genetic mutations. These EBs contained biological information relevant to the variability in the pathophysiological and clinical aspects of genetic FTLD, and for offering valuable guidance in complex clinical decision-making, such as decisions related to genetic testing.

Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum

BACKGROUND

TDP-43 proteinopathies represent a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum.

METHODS

We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease.

RESULTS

SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 types B, E and C. In contrast, the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. The overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology.

CONCLUSIONS

Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.

Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum

Background

TDP-43 proteinopathies represents a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum.

Methods

We used a data-driven procedure to identify 13 anatomic clusters of brain volumes for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease.

Results

SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy either in prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The Limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 type B, E and C. In contrast, the Prefrontal/Somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. Overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology.

Conclusions

Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.

Functional and structural network changes related with cognition in semantic dementia longitudinally

Longitudinal changes in the white matter/functional brain networks of semantic dementia (SD), as well as their relations with cognition remain unclear. Using a graph-theoretic method, we examined the neuroimaging (T1, diffusion tensor imaging, functional MRI) network properties and cognitive performance in processing semantic knowledge of general and six modalities (i.e., object form, color, motion, sound, manipulation and function) from 31 patients (at two time points with an interval of 2 years) and 20 controls (only at baseline). Partial correlation analyses were carried out to explore the relationships between the network changes and the declines of semantic performance. SD exhibited aberrant general and modality-specific semantic impairment, and gradually worsened over time. Overall, the brain networks showed a decreased global and local efficiency in the functional network organization but a preserved structural network organization with a 2-year follow-up. With disease progression, both structural and functional alterations were found to be extended to the temporal and frontal lobes. The regional topological alteration in the left inferior temporal gyrus (ITG.L) was significantly correlated with general semantic processing. Meanwhile, the right superior temporal gyrus and right supplementary motor area were identified to be associated with color and motor-related semantic attributes. SD manifested disrupted structural and functional network pattern longitudinally. We proposed a hub region (i.e., ITG.L) of semantic network and distributed modality-specific semantic-related regions. These findings support the hub-and-spoke semantic theory and provide targets for future therapy.

Single-cell RNA-seq reveals alterations in peripheral CX3CR1 and nonclassical monocytes in familial tauopathy

BACKGROUND

Emerging evidence from mouse models is beginning to elucidate the brain's immune response to tau pathology, but little is known about the nature of this response in humans. In addition, it remains unclear to what extent tau pathology and the local inflammatory response within the brain influence the broader immune system.

METHODS

To address these questions, we performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from carriers of pathogenic variants in MAPT, the gene encoding tau (n = 8), and healthy non-carrier controls (n = 8). Primary findings from our scRNA-seq analyses were confirmed and extended via flow cytometry, droplet digital (dd)PCR, and secondary analyses of publicly available transcriptomics datasets.

RESULTS

Analysis of ~ 181,000 individual PBMC transcriptomes demonstrated striking differential expression in monocytes and natural killer (NK) cells in MAPT pathogenic variant carriers. In particular, we observed a marked reduction in the expression of CX3CR1-the gene encoding the fractalkine receptor that is known to modulate tau pathology in mouse models-in monocytes and NK cells. We also observed a significant reduction in the abundance of nonclassical monocytes and dysregulated expression of nonclassical monocyte marker genes, including FCGR3A. Finally, we identified reductions in TMEM176A and TMEM176B, genes thought to be involved in the inflammatory response in human microglia but with unclear function in peripheral monocytes. We confirmed the reduction in nonclassical monocytes by flow cytometry and the differential expression of select biologically relevant genes dysregulated in our scRNA-seq data using ddPCR.

CONCLUSIONS

Our results suggest that human peripheral immune cell expression and abundance are modulated by tau-associated pathophysiologic changes. CX3CR1 and nonclassical monocytes in particular will be a focus of future work exploring the role of these peripheral signals in additional tau-associated neurodegenerative diseases.

Source space connectomics of neurodegeneration: One-metric approach does not fit all

Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.

Model-based whole-brain perturbational landscape of neurodegenerative diseases

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

Brain PET Imaging: Frontotemporal Dementia

Brain PET adds value in diagnosing neurodegenerative disorders, especially frontotemporal dementia (FTD) due to its syndromic presentation that overlaps with a variety of other neurodegenerative and psychiatric disorders. 18F-FDG-PET has improved sensitivity and specificity compared with structural MR imaging, with optimal diagnostic results achieved when both techniques are utilized. PET demonstrates superior sensitivity compared with SPECT for FTD diagnosis that is primarily a supplement to other imaging and clinical evaluations. Tau-PET and amyloid-PET primary use in FTD diagnosis is differentiation from Alzheimer disease, although these methods are limited mainly to research settings.

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

Incongruent gray matter atrophy and functional connectivity of striatal subregions in behavioral variant frontotemporal dementia

Previous studies on the striatum demonstrated that it is involved in the regulation of cognitive function and psychiatric symptoms in patients with behavioral variant frontotemporal dementia (bvFTD). Multiple lines of evidence have shown that striatal subregions have their own functions. However, the results of the existing studies on striatal subregions are inconsistent and unclear. In this study, we found that structural imaging analysis revealed that the bvFTD patients had smaller volumes of striatal subregions than the controls. We found that the degree of atrophy varied across the striatal subregions. Additionally, the right striatal subregions were significantly more atrophic than the left in bvFTD. Functional imaging analysis revealed that bvFTD patients exhibited different changed patterns of resting-state functional connectivity (RSFC) when striatal subregions were selected as regions of interest (ROI). The RSFC extending range on the right ROIs was more significant than on the left in the same subregion. Interestingly, the RSFC of the subregions extending to the insula were consistent. In addition, the left dorsolateral putamen may be involved in executive function regulation. This suggests that incongruence in striatal subregions may be critical to the bvFTD characteristics.

Intracellular energy controls dynamics of stress-induced ribonucleoprotein granules

Energy metabolism and membraneless organelles have been implicated in human diseases including neurodegeneration. How energy deficiency regulates ribonucleoprotein particles such as stress granules (SGs) is still unclear. Here we identified a unique type of granules induced by energy deficiency under physiological conditions and uncovered the mechanisms by which the dynamics of diverse stress-induced granules are regulated. Severe energy deficiency induced the rapid formation of energy deficiency-induced stress granules (eSGs) independently of eIF2α phosphorylation, whereas moderate energy deficiency delayed the clearance of conventional SGs. The formation of eSGs or the clearance of SGs was regulated by the mTOR-4EBP1-eIF4E pathway or eIF4A1, involving assembly of the eIF4F complex or RNA condensation, respectively. In neurons or brain organoids derived from patients carrying the C9orf72 repeat expansion associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), the eSG formation was enhanced, and the clearance of conventional SGs was impaired. These results reveal a critical role for intracellular energy in the regulation of diverse granules and suggest that disruptions in energy-controlled granule dynamics may contribute to the pathogenesis of relevant diseases.

Stress granules are associated with neurodegenerative diseases. Here, Wang et al. found intracellular energy deficiencies trigger a unique type of granules and disrupt granule disassembly through 4EBP1/eIF4A.

Enlarged perivascular spaces and white matter hyperintensities in patients with frontotemporal lobar degeneration syndromes

Objective

The aim of this study was to investigate the distribution characteristics of enlarged perivascular spaces (EPVS) and white matter hyperintensities (WMH) and their associations with disease severity across the frontotemporal lobar degeneration (FTLD) syndromes spectrum.

Methods

This study included 73 controls, 39 progressive supranuclear palsy Richardson’s syndrome (PSP-RS), 31 corticobasal syndrome (CBS), 47 behavioral variant frontotemporal dementia (bvFTD), 36 non-fluent variant primary progressive aphasia (nfvPPA), and 50 semantic variant primary progressive aphasia (svPPA). All subjects had brain magnetic resonance imaging (MRI) and neuropsychological tests, including progressive supranuclear palsy rating scale (PSPRS) and FTLD modified clinical dementia rating sum of boxes (FTLD-CDR). EPVS number and grade were rated on MRI in the centrum semiovale (CSO-EPVS), basal ganglia (BG-EPVS), and brain stem (BS-EPVS). Periventricular (PWMH) and deep (DWMH) were also graded on MRI. The distribution characteristics of EPVS and WMH were compared between control and disease groups. Multivariable linear regression analysis was performed to evaluate the association of EPVS and WMH with disease severity.

Results

Compared with control subjects, PSP-RS and CBS had more BS-EPVS; CBS, bvFTD, and nfvPPA had less CSO-EPVS; all disease groups except CBS had higher PWMH (p < 0.05). BS-EPVS was associated with PSPRS in PSP-RS (β = 2.395, 95% CI 0.888–3.901) and CBS (β = 3.115, 95% CI 1.584–4.647). PWMH was associated with FTLD-CDR in bvFTD (β = 1.823, 95% CI 0.752–2.895), nfvPPA (β = 0.971, 95% CI 0.030–1.912), and svPPA (OR: 1.330, 95% CI 0.457–2.204).

Conclusion

BS-EPVS could be a promising indicator of disease severity in PSP-RS and CBS, while PWMH could reflect the severity of bvFTD, nfvPPA, and svPPA.

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.

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.