Development of a Speech-based Composite Score for Remotely Quantifying Language Changes in Frontotemporal Dementia.

"Frontotemporal dementia - Clinical and pathophysiological aspects." Nordic Journal of Psychiatry, 54(2), pp. 149–150

Frontotemporal dementia (FTD) is a prevalent neurodegenerative disease of heterogeneous histopathology. Neuropathological subtypes are identified on the basis of the presence or absence of tau- or ubiquitin-positive neuronal inclusions. Our recent work has established four disease stages that are independent of neuropathological subtype and reflect the clinical and degenerative progression observed in FTD. The variability in the extent of neuronal loss, astrogliosis, and microvacuolation are, therefore, more likely to reflect disease stage with potentially predictable differences between cases at early versus late disease stages. Understanding the variability in these parameters may assist in determining the importance of diverse disease subtypes in FTD. We examined 21 cases of sporadic, behavioural variant FTD and quantified the progression of histopathological change. The neuropathology of early disease was marked by severe astrogliosis of both the frontal and temporal cortices and neuronal loss, which was more evident in upper cortical layers of the frontal lobe. In late disease, neuronal loss was evident from both layer III and V in frontal and temporal cortices, and particularly the CA1 sector of the hippocampus. In addition, we compared the neuropathology of Pick's disease, dementia lacking distinctive histopathology and FTD with motor neuron disease, and found no difference in these pathological subtypes on the basis of neuronal loss, astrogliosis or microvacuolation. These results show that the earliest cellular changes in FTD occur in glia, and that disease stage rather than FTD subtype determines the pattern and extent of neuronal degeneration.

Frontotemporal dementia (FTD) presents a complex spectrum of neurodegenerative disorders, encompassing distinct subtypes with varied clinical manifestations. This study investigates alterations in brain module organization associated with FTD subtypes using connectome analysis, aiming to identify potential biomarkers and enhance subtype prediction. Resting-state functional magnetic resonance imaging data were obtained from 41 individuals with behavioral variant frontotemporal dementia (BV-FTD), 32 with semantic variant frontotemporal dementia (SV-FTD), 28 with progressive non-fluent aphasia frontotemporal dementia (PNFA-FTD), and 94 healthy controls. Individual functional brain networks were constructed at the voxel level and binarized based on density thresholds. Modular segregation index (MSI) and participation coefficient (PC) were calculated to assess module integrity and identify regions with altered nodal properties. The relationship between modular measures and clinical scores was examined, and machine learning models were developed for subtype prediction. Both BV-FTD and SV-FTD groups exhibited decreased MSI in the subcortical module (SUB), default mode network (DMN), and ventral attention network (VAN) compared to healthy controls. Additionally, BV-FTD specifically displayed disrupted frontoparietal network (FPN) integrity compared to other FTD subtypes and controls. All FTD subtypes showed increased PC values in the insular region and reduced connections between the insular and VAN/FPN compared to controls. Moreover, significant associations between specific network alterations and clinical variables were observed. Machine learning models utilizing these matrices achieved high performance in differentiating FTD subtypes. This pilot study reveals diverse brain module organization across FTD subtypes, shedding light on both shared and distinct neurobiological underpinnings of the disorder.

BackgroundClinical diagnosis of frontotemporal dementia (FTD) can be challenging, requiring an accurate tool dedicated to this diagnostic hurdle. Since FTD exhibits distinct regional atrophy patterns on magnetic resonance imaging (MRI), AI‐aided automated brain volume analysis could enhance the clinical diagnostic assessment of FTD, including the detection of the disease and the classification of subtypes, which encompass behavioral variant (BV), semantic variant (SV), and progressive non‐fluent aphasia (PNFA). In this study, we leverage automated brain volumetry software to approach both FTD detection and the differential diagnosis among its subtypes.MethodA total 258 patients with FTD (BV = 57, SV = 40, PNFA = 34) and 127 Normal Control (NC) were identified from the frontotemporal lobar degeneration neuroimaging initiative (NIFD) data sets with available MRI. We obtained 104 areas of patient’s normalized regional brain volumes using VUNO‐Med DeepBrain. Comparative analyses were conducted to explore differences in regional volume distribution between FTD and NC, as well as among FTD subtype based on their known atrophy patterns. Additionally, regional normative percentiles were utilized as features for classification using the XGBoost algorithm to distinguish FTD vs NC, BV vs SV vs PNFA, and BV vs SV vs PNFA vs NC.ResultIn BV, significant differences in atrophy patterns were observed in the frontal lobe, demonstrating an anterior‐posterior gradient compared to SV, PNFA and NC (P<0.01). SV exhibited left side dominant asymmetric atrophy, particularly in the inferior temporal lobe, compared to BV, PNFA, and NC (P<0.01). Additionally, the inferior horn of the lateral ventricle in SV had higher volume than other subtypes, attributable to atrophy of surrounding brain regions. In contrast, the differences in atrophy patterns for PNFA were statistically less significant when compared to other FTD subtypes. Automated brain volumetry software‐based classifications demonstrated high accuracy of FTD vs NC (Accuracy 95% CI = [0.88, 0.89]), BV vs SV vs PNFA ([0.79, 0.81]), and BV vs SV vs PNFA vs NC ([0.82, 0.83]).ConclusionOur study demonstrates the effectiveness of automated brain volumetry and machine learning in identifying FTD and differentiating frontotemporal dementia subtypes, contributing to an enhanced understanding of subtypes and improved diagnostic precision in neurodegenerative disorders.

To compare the sensitivity of structural MRI and 18F-fludeoxyglucose PET (18FDG-PET) to detect longitudinal changes in frontotemporal dementia (FTD). Thirty patients with behavioral variant FTD (bvFTD), 7 with nonfluent/agrammatic variant primary progressive aphasia (nfvPPA), 16 with semantic variant primary progressive aphasia (svPPA), and 43 cognitively normal controls underwent 2-4 MRI and 18FDG-PET scans (total scans/visit = 270) as part of the Frontotemporal Lobar Degeneration Neuroimaging Initiative study. Linear mixed-effects models were carried out voxel-wise and in regions of interest to identify areas showing decreased volume or metabolism over time in patients as compared to controls. At baseline, patients with bvFTD showed bilateral temporal, dorsolateral, and medial prefrontal atrophy/hypometabolism that extended with time into adjacent structures and parietal lobe. In nfvPPA, baseline atrophy/hypometabolism in supplementary motor cortex extended with time into left greater than right precentral, dorsolateral, and dorsomedial prefrontal cortex. In svPPA, baseline atrophy/hypometabolism encompassed the anterior temporal and medial prefrontal cortex and longitudinal changes were found in temporal, orbitofrontal, and lateral parietal cortex. Across syndromes, there was substantial overlap in the brain regions showing volume and metabolism loss. Even though the pattern of metabolic decline was more extensive, metabolic changes were also more variable and sample size estimates were similar or higher for 18FDG-PET compared to MRI. Our findings demonstrated the sensitivity of 18FDG-PET and structural MRI for tracking disease progression in FTD. Both modalities showed highly overlapping patterns of longitudinal change and comparable sample size estimates to detect longitudinal changes in future clinical trials.

Computational imaging and quantitative biomarkers offer invaluable insights in the pre-symptomatic phase of neurodegenerative conditions several years before clinical manifestation. In recent years, there has been a focused effort to characterize pre-symptomatic cerebral changes in familial frontotemporal dementias using computational imaging. Accordingly, a systematic literature review was conducted of original articles investigating pre-symptomatic imaging changes in frontotemporal dementia focusing on study design, imaging modalities, data interpretation, control cohorts and key findings. The review is limited to the most common genotypes: chromosome 9 open reading frame 72 (C9orf72), progranulin (GRN), or microtubule-associated protein tau (MAPT) genotypes. Sixty-eight studies were identified with a median sample size of 15 (3–141) per genotype. Only a minority of studies were longitudinal (28%; 19/68) with a median follow-up of 2 (1–8) years. MRI (97%; 66/68) was the most common imaging modality, and primarily grey matter analyses were conducted (75%; 19/68). Some studies used multimodal analyses 44% (30/68). Genotype-associated imaging signatures are presented, innovative study designs are highlighted, common methodological shortcomings are discussed and lessons for future studies are outlined. Emerging academic observations have potential clinical implications for expediting the diagnosis, tracking disease progression and optimising the timing of pharmaceutical trials.

Impaired acquisition rates of probabilistic associative learning in frontotemporal dementia is associated with fronto-striatal atrophy.

Frontotemporal dementia is a group of neurodegenerative disorders mainly characterized by behavioural and language impairments. While the precise pathophysiology remains elusive, emerging evidence points to an important role of dopamine dysfunction, particularly within the caudate nucleus. Moreover, a theoretical model proposes that frontotemporal dementia manifestations result from a deficit in goal-directed behaviour, which may be related to altered dopamine control of the frontostriatal circuitry. However, no study has investigated the gradient of striatal dopamine transporter levels in frontotemporal dementia using neuroimaging and their correlation with clinical features. This study used 123I-Ioflupane Single Photon Emission Computed Tomography imaging to measure striatal dopamine transporter levels and their distribution patterns in frontotemporal dementia, compared to Parkinson’s disease and healthy controls. Additionally, we explored the correlation between dopamine transporter uptake and two key domains affected in frontotemporal dementia: social cognition and language abilities. We hypothesized that frontotemporal dementia would show a predominant dopaminergic deficit in the caudate, and that this would correlate with the severity of clinical core features. The study comprised 139 participants, including 34 sporadic and genetic frontotemporal dementia, 68 Parkinson’s disease individuals, and 37 age- and sex-matched healthy controls. Among the frontotemporal dementia group, 22 cases had clinically probable behavioural variant frontotemporal dementia, and 12 had primary progressive aphasia. Social cognition was assessed with the abbreviated version of the Social and Emotional Assessment, which includes a Theory of Mind test and a Facial Emotion Recognition Task. Language skills were evaluated with the Screening for Aphasia in NeuroDegeneration battery. We found that dopamine transporter levels were reduced in frontotemporal dementia compared to healthy controls (P < 0.001) and that frontotemporal dementia showed a higher putamen-to-caudate ratio than Parkinson’s disease (P < 0.001), particularly notable in patients with identified disease-causing mutation. We also found that dopamine transporter levels were correlated with parkinsonian motor features and general cognition in frontotemporal dementia. Notably, both social cognition—especially facial emotion recognition—and language abilities exhibited associations with dopamine transporter levels in both the putamen and the caudate. These findings suggest that the pattern of dopamine transporter uptake could serve as a valuable biomarker for frontotemporal dementia, shedding light on the role of the dopaminergic system and the striatum in some fundamental clinical aspects. This opens new avenues for further investigating the underlying mechanisms and potential therapeutic targets of the dopaminergic projections in frontotemporal dementia.

BackgroundFrontotemporal dementia (FTD) is recognised as a clinically and morphologically heterogeneous group of interrelated neurodegenerative conditions. One of the subtypes within this disease spectrum is the behavioural variant FTD (bvFTD). This is known to be a varied disorder with a mixture of tau-positive and tau-negative underlying pathologies. The other subtypes include semantic dementia (SD), which generally exhibits tau-negative pathology, and progressive non-fluent aphasia (PNFA), which is usually tau-positive. As the clinical presentation of these subtypes may overlap, a specific diagnosis can be difficult to attain and today no specific biomarker can predict the underlying pathology. Neurofilament light chain protein (NFL), a cytoskeletal constituent of intermediate filaments, is thought to reflect neuronal and axonal death when appearing in the cerebrospinal fluid (CSF). NFL has been shown to be elevated in CSF in patients with FTD compared with AD and controls. Our hypothesis was that the levels of NFL also differ between the subtypes of FTD and may indicate the underlying pathological subtype.MethodsWe retrospectively analysed data from previous CSF analyses in 34 FTD cases (23 bvFTD, seven SD, four PNFA), 20 AD cases, and 26 healthy controls. A separate group of 10 neuropathologically verified and subtyped FTD cases (seven tau-negative, three tau-positive) were also analysed.ResultNFL levels were significantly higher in FTD compared with both AD (p<0.001) and controls (p<0.001). The NFL levels of SD and bvFTD were significantly higher (p<0.001) compared with AD. The biomarker profiles of PNFA and AD were similar. In the neuropathologically verified FTD cases, NFL was higher in the tau-negative than in the tau-positive cases (exact p=0.017).ConclusionsThe marked NFL elevation in some but not all FTD cases is likely to reflect the different underlying pathologies. The highest NFL values found in the SD group as well as in the neuropathologically verified tau-negative cases may be of subtype diagnostic value, if corroborated in larger patient cohorts. In bvFTD, a mixture of tau-positive and tau-negative underlying pathologies could possibly explain the intermediate NFL values.

BackgroundAdvances in the understanding of the pathophysiology of frontotemporal dementia (FTD) and related disorders, along with the development of novel candidate disease modifying treatments, have stimulated the need for tools to assess the efficacy of new therapies. While perfusion imaging by arterial spin labeling (ASL) is an attractive approach for longitudinal imaging biomarkers of neurodegeneration, sources of variability between sessions including arterial transit times (ATT) and fluctuations in resting perfusion can reduce its sensitivity. Establishing the magnitude of perfusion changes that can be reliably detected is necessary to delineate longitudinal perfusion changes related to disease processes from the effects of these sources of error. PurposeTo assess the feasibility of ASL for longitudinal monitoring of patients with FTD by quantifying between-session variability of perfusion on a voxel-by-voxel basis. Methods and materialsASL data were collected in 13 healthy controls and 8 patients with FTD or progressive supra-nuclear palsy. Variability in cerebral blood flow (CBF) by single delay pseudo-continuous ASL (SD-pCASL) acquired one month apart were quantified by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). Additionally, CBF by SD-pCASL and ATT by low-resolution multiple inversion time ASL (LowRes-pCASL) were compared to Hadamard encoded sequences which are able to simultaneously measure CBF and ATT with improved time-efficiency. ResultsAgreement of grey-matter perfusion between sessions was found for both patients and controls (CV = 10.8% and 8.3% respectively) with good reliability for both groups (ICC > 0.6). Intensity normalization to remove day-to-day fluctuations in resting perfusion reduced the CV by 28%. Less than 5% of voxels had ATTs above the chosen post labelling delay (2 s), indicating that the ATT was not a significant source of error. Hadamard-encoded perfusion imaging yielded systematically higher CBF compared to SD-pCASL, but produced similar transit-time measurements. Power analysis revealed that SD-pCASL has the sensitivity to detect longitudinal changes as low as 10% with as few as 10 patient participants. ConclusionWith the appropriate labeling parameters, SD-pCASL is a promising approach for assessing longitudinal changes in CBF associated with FTD.

Behavioural variant frontotemporal dementia is characterized by a change in comportment. It is associated with considerable functional decline over the course of the illness albeit with sometimes dramatic variability among patients. It is unknown whether any baseline features, or combination of features, could predict rate of functional decline in behavioural variant frontotemporal dementia. The aim of this study was to investigate the effects of different baseline clinical, neuropsychological, neuropsychiatric, genetic and anatomic predictors on the rate of functional decline as measured by the Clinical Dementia Rating Sum of Boxes scale. We identified 86 subjects with behavioural variant frontotemporal dementia that had multiple serial Clinical Dementia Rating Sum of Boxes assessments (mean 4, range 2-18). Atlas-based parcellation was used to generate volumes for specific regions of interest at baseline. Volumes were utilized to classify subjects into different anatomical subtypes using the advanced statistical technique of cluster analysis and were assessed as predictor variables. Composite scores were generated for the neuropsychological domains of executive, language, memory and visuospatial function. Behaviours from the brief questionnaire form of the Neuropsychiatric Inventory were assessed. Linear mixed-effects regression modelling was used to determine which baseline features predict rate of future functional decline. Rates of functional decline differed across the anatomical subtypes of behavioural variant frontotemporal dementia, with faster rates observed in the frontal dominant and frontotemporal subtypes. In addition, subjects with poorer performance on neuropsychological tests of executive, language and visuospatial function, less disinhibition, agitation/aggression and night-time behaviours at presentation, and smaller medial, lateral and orbital frontal lobe volumes showed faster rates of decline. In many instances, the effect of the predictor variables observed across all subjects was also preserved within anatomical subtypes. Furthermore, some of the predictor variables improved our prediction of rate of functional decline after anatomical subtype was taken into account. In particular, age at onset was a highly significant predictor but only after adjusting for subtype. We also found that although some predictor variables, for example gender, Mini-Mental State Examination score, and apathy/indifference, did not affect the rate of functional decline; these variables were associated with the actual Clinical Dementia Rating Sum of Boxes score estimated for any given time-point. These findings suggest that in behavioural variant frontotemporal dementia, rate of functional decline is driven by the combination of anatomical pattern of atrophy, age at onset, and neuropsychiatric characteristics of the subject at baseline.

Clinical aspects of hereditary frontotemporal dementia

C9orf72 and intracerebral hemorrhage

Objectives: Frontotemporal Dementia (FTD) is an uncommon type of dementia. The hallmark feature of FTD is the presentation with aphasia or behavioral changes which vary in different FTD subtypes. In this study, we propose a quantitative aphasia test as an additive diagnostic tool for differentiation of FTD subtypes. Methods: The applied study was conducted on 20 patients with FTD (13 men and 7 women) aged 58-78 years (Mean age=63±0.8 years) referred to dementia clinic of Rasul Akram Hospital in Tehran, Iran. Based on clinical diagnosis, patients were divided into three groups of behavioral variant (n=5), semantic dementia (n=4) and non-fluent aphasia (n=11). The Persian Western Aphasia Battery (P-WAB-1) test was performed on the patients to assess their aphasia severity based on its overall score (Aphasia Quotient). The score of each language section of the test was also reported for each study group, separately. After recording data, they were analyzed in SPSS v. 22 software using the whole rock analysis. Significance level of Aphasia Quotient score was measured separately for each study groups. Results: Patients with non-fluent aphasia had problems only in the speech fluency domain, and their abilities in other domains remained intact. Conclusion: It seems that P-WAB-1 test is a useful tool for assessing the non-fluent aphasia in patients who suffer from progressive dementia. These patients should be referred to speech therapist for rehabilitation.

Speech perception plays a significant role in the development of speech and language ability in children with hearing loss. There are various speech perception tests with the specific protocol to administer and score. Auditory evoked potential is an objective measure used for assessing hearing acuity in young children. Higher cortical evoked potential test provides an effective indication of the physiological status of auditory cortex. CEAP can be obtained in young children with assistive devices. The present research study aims to investigate whether latency and amplitude of aided cortical evoked potential can predict Speech perception score in cochlear implant users. 52 cochlear implant users and 102 typical children were tested. The mean age for children using cochlear implant was 8.05 years, standard deviation 1.4. The normal hearing group had a mean age of 8.12 years and standard deviation 1.46. Cochlear implant users were having at least 15 active electrodes. Speech stimulus was used with 100 ms duration, 20 ms rise/fall, 20 ms plateau tones. The inter-stimulus interval was kept at 1125 ms. During statistical analysis of data, CEAP P1 latencies and speech perception scores were found to be related. A further regression equation was obtained. The present study reveals speech stimuli can evoke distinct neural response patterns from auditory cortex. The present research study also helps in understanding the neural processing of speech in individuals with hearing impairment using Cochlear implant. Statistical analysis of data CEAP P1 latency and speech perception score regression equation were obtained. The result reveals that speech stimuli can evoke distinct neural response patterns from auditory cortex. CEAP finding support speech perception ability and auditory evoked potential help to the study of neural processing of speech in individuals with the cochlear implant. The evoked potential and speech perception ability has a significant relation between them.