Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET.

BACKGROUND: The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. METHODS: We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. RESULTS: The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). CONCLUSION: Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Combined in vivo MRI assessment of locus coeruleus and nucleus basalis of Meynert integrity in amnestic Alzheimer's disease, suspected-LATE and frontotemporal dementia.

BACKGROUND: The locus coeruleus (LC) and the nucleus basalis of Meynert (NBM) are altered in early stages of Alzheimer's disease (AD). Little is known about LC and NBM alteration in limbic-predominant age-related TDP-43 encephalopathy (LATE) and frontotemporal dementia (FTD). The aim of the present study is to investigate in vivo LC and NBM integrity in patients with suspected-LATE, early-amnestic AD and FTD in comparison with controls. METHODS: Seventy-two participants (23 early amnestic-AD patients, 17 suspected-LATE, 17 FTD patients, defined by a clinical-biological diagnosis reinforced by amyloid and tau PET imaging, and 15 controls) underwent neuropsychological assessment and 3T brain MRI. We analyzed the locus coeruleus signal intensity (LC-I) and the NBM volume as well as their relation with cognition and with medial temporal/cortical atrophy. RESULTS: We found significantly lower LC-I and NBM volume in amnestic-AD and suspected-LATE in comparison with controls. In FTD, we also observed lower NBM volume but a slightly less marked alteration of the LC-I, independently of the temporal or frontal phenotype. NBM volume was correlated with the global cognitive efficiency in AD patients. Strong correlations were found between NBM volume and that of medial temporal structures, particularly the amygdala in both AD and FTD patients. CONCLUSIONS: The alteration of LC and NBM in amnestic-AD, presumed-LATE and FTD suggests a common vulnerability of these structures to different proteinopathies. Targeting the noradrenergic and cholinergic systems could be effective therapeutic strategies in LATE and FTD.

Longitudinal cerebral perfusion in presymptomatic genetic frontotemporal dementia: GENFI results.

INTRODUCTION: Effective longitudinal biomarkers that track disease progression are needed to characterize the presymptomatic phase of genetic frontotemporal dementia (FTD). We investigate the utility of cerebral perfusion as one such biomarker in presymptomatic FTD mutation carriers. METHODS: We investigated longitudinal profiles of cerebral perfusion using arterial spin labeling magnetic resonance imaging in 42 C9orf72, 70 GRN, and 31 MAPT presymptomatic carriers and 158 non-carrier controls. Linear mixed effects models assessed perfusion up to 5 years after baseline assessment. RESULTS: Perfusion decline was evident in all three presymptomatic groups in global gray matter. Each group also featured its own regional pattern of hypoperfusion over time, with the left thalamus common to all groups. Frontal lobe regions featured lower perfusion in those who symptomatically converted versus asymptomatic carriers past their expected age of disease onset. DISCUSSION: Cerebral perfusion is a potential biomarker for assessing genetic FTD and its genetic subgroups prior to symptom onset. HIGHLIGHTS: Gray matter perfusion declines in at-risk genetic frontotemporal dementia (FTD). Regional perfusion decline differs between at-risk genetic FTD subgroups . Hypoperfusion in the left thalamus is common across all presymptomatic groups. Converters exhibit greater right frontal hypoperfusion than non-converters past their expected conversion date. Cerebral hypoperfusion is a potential early biomarker of genetic FTD.

Distinct neural signatures of pulvinar in C9orf72 amyotrophic lateral sclerosis mutation carriers and noncarriers.

BACKGROUND AND PURPOSE: Thalamic alterations have been reported as a major feature in presymptomatic and symptomatic patients carrying the C9orf72 mutation across the frontotemporal dementia-amyotrophic lateral sclerosis (ALS) spectrum. Specifically, the pulvinar, a high-order thalamic nucleus and timekeeper for large-scale cortical networks, has been hypothesized to be involved in C9orf72-related neurodegenerative diseases. We investigated whether pulvinar volume can be useful for differential diagnosis in ALS C9orf72 mutation carriers and noncarriers and how underlying functional connectivity changes affect this region. METHODS: We studied 19 ALS C9orf72 mutation carriers (ALSC9+) accurately matched with wild-type ALS (ALSC9-) and ALS mimic (ALSmimic) patients using structural and resting-state functional magnetic resonance imaging data. Pulvinar volume was computed using automatic segmentation. Seed-to-voxel functional connectivity analyses were performed using seeds from a pulvinar functional parcellation. RESULTS: Pulvinar structural integrity had high discriminative values for ALSC9+ patients compared to ALSmimic (area under the curve [AUC] = 0.86) and ALSC9- (AUC = 0.77) patients, yielding a volume cutpoint of approximately 0.23%. Compared to ALSmimic, ALSC9- showed increased anterior, inferior, and lateral pulvinar connections with bilateral occipital-temporal-parietal regions, whereas ALSC9+ showed no differences. ALSC9+ patients when compared to ALSC9- patients showed reduced pulvinar-occipital connectivity for anterior and inferior pulvinar seeds. CONCLUSIONS: Pulvinar volume could be a differential biomarker closely related to the C9orf72 mutation. A pulvinar-cortical circuit dysfunction might play a critical role in disease progression and development, in both the genetic phenotype and ALS wild-type patients.

Cross-sectional study of patients with VCP multisystem proteinopathy 1 using dual-energy x-ray absorptiometry.

INTRODUCTION/AIMS: VCP multisystem proteinopathy 1 (MSP1), encompassing inclusion body myopathy (IBM), Paget's disease of bone (PDB) and frontotemporal dementia (FTD) (IBMPFD), features progressive muscle weakness, fatty infiltration, and disorganized bone structure in Pagetic bones. The aim of this study is to utilize dual-energy x-ray absorptiometry (DXA) parameters to examine it as a biomarker of muscle and bone disease in MSP1. METHODS: DXA scans were obtained in 28 patients to assess body composition parameters (bone mineral density [BMD], T-score, total fat, and lean mass) across different groups: total VCP disease (n = 19), including myopathy without Paget's ("myopathy"; n = 12) and myopathy with Paget's ("Paget"; n = 7), and unaffected first-degree relatives serving as controls (n = 6). RESULTS: In the VCP disease group, significant declines in left hip BMD and Z-scores were noted versus the control group (p ≤ .03). The VCP disease group showed decreased whole body lean mass % (p = .04), and increased total body fat % (p = .04) compared to controls. Subgroup comparisons indicated osteopenia in 33.3% and osteoporosis in 8.3% of the myopathy group, with 14.3% exhibiting osteopenia in the Paget group. Moreover, the Paget group displayed higher lumbar L1-L4 T-score values than the myopathy group. DISCUSSION: In MSP1, DXA revealed reduced bone and lean mass, and increased fat mass. These DXA insights could aid in monitoring disease progression of muscle loss and secondary osteopenia/osteoporosis in MSP1, providing value both clinically and in clinical research.

Bone scan findings of Paget's disease of bone in patients with VCP Multisystem Proteinopathy 1.

Multisystem Proteinopathy 1 (MSP1) disease is a rare genetic disorder caused by mutations in the Valosin-Containing Protein (VCP) gene with clinical features of inclusion body myopathy (IBM), frontotemporal dementia (FTD), and Paget's disease of bone (PDB). We performed bone scan imaging in twelve patients (6 females, 6 males) with confirmed VCP gene mutation six (50%) of which has myopathy alone, four (33%) with both PDB and myopathy, and two (15%) were presymptomatic carriers. We aim to characterize the PDB in diagnosed individuals, and potentially identify PDB in the myopathy and presymptomatic groups. Interestingly, two patients with previously undiagnosed PDB had positive diagnostic findings on the bone scan and subsequent radiograph imaging. Among the individuals with PDB, increased radiotracer uptake of the affected bones were of typical distribution as seen in conventional PDB and those reported in other MSP1 cohorts which are the thoracic spine and ribs (75%), pelvis (75%), shoulder (75%) and calvarium (15%). Overall, we show that technetium-99m bone scans done at regular intervals are a sensitive screening tool in patients with MSP1 associated VCP variants at risk for PDB. However, diagnostic confirmation should be coupled with clinical history, biochemical analysis, and skeletal radiographs to facilitate early treatment and prevention complications, acknowledging its limited specificity.

A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET.

With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.

Narcissistic Personality Disorder as Prodromal Feature of Early-Onset, GRN-Positive bvFTD: A Case Report.

Background: Behavioral variant frontotemporal dementia (bvFTD) typically involves subtle changes in personality that can delay a timely diagnosis. Objective: Here, we report the case of a patient diagnosed of GRN-positive bvFTD at the age of 52 presenting with a 7-year history of narcissistic personality disorder, accordingly to DSM-5 criteria. Methods: The patient was referred to neurological and neuropsychological examination. She underwent 3 Tesla magnetic resonance imaging (MRI) and genetic studies. Results: The neuropsychological examination revealed profound deficits in all cognitive domains and 3T brain MRI showed marked fronto-temporal atrophy. A mutation in the GRN gene further confirmed the diagnosis. Conclusions: The present case documents an unusual onset of bvFTD and highlights the problematic nature of the differential diagnosis between prodromal psychiatric features of the disease and primary psychiatric disorders. Early recognition and diagnosis of bvFTD can lead to appropriate management and support for patients and their families. This case highlights the importance of considering neurodegenerative diseases, such as bvFTD, in the differential diagnosis of psychiatric disorders, especially when exacerbations of behavioral traits manifest in adults.

Sleep and circadian rhythm disruptions in behavioral variant frontotemporal dementia.

INTRODUCTION: Sleep and rest-activity rhythm alterations are common in neurodegenerative diseases. However, their characterization in patients with behavioral variant frontotemporal dementia (bvFTD) has proven elusive. We investigated rest-activity rhythm alterations, sleep disturbances, and their neural correlates in bvFTD. METHODS: Twenty-seven bvFTD patients and 25 healthy controls completed sleep questionnaires and underwent 7 days of actigraphy while concurrently maintaining a sleep diary. Cortical complexity and thickness were calculated from T1-weighted magnetic resonance (MR) images. RESULTS: Compared to controls, bvFTD patients showed longer time in bed (95% confidence interval [CI]: 79.31, 321.83) and total sleep time (95% CI: 24.38, 321.88), lower sleep efficiency (95% CI: -12.58, -95.54), and rest-activity rhythm alterations in the morning and early afternoon. Increased sleep duration was associated with reduced cortical thickness in frontal regions. DISCUSSION: Patients with bvFTD showed longer sleep duration, lower sleep quality, and rest-activity rhythm alterations. Actigraphy could serve as a cost-effective and accessible tool for ecologically monitoring changes in sleep duration in bvFTD patients. HIGHLIGHTS: We assessed sleep and circadian rhythms in behavioral variant frontotemporal dementia (bvFTD) using actigraphy. Patients with bvFTD show increased sleep duration and reduced sleep quality. Patients with bvFTD show rest-activity alterations in the morning and early afternoon. Sleep duration is associated with reduced cortical thickness in frontal regions. These alterations may represent an early sign of neurodegeneration.

The role of brain perfusion SPECT in the diagnosis of frontotemporal dementia: A systematic review.

BACKGROUND AND PURPOSE: Frontotemporal dementia (FTD) is the second most common cause of presenile dementia. The clinical distinction between FTD, Alzheimer's disease (AD), and other dementias is a clinical challenge. Brain perfusion SPECT may contribute to the diagnosis of FTD, but its value is unclear. METHODS: We performed a systematic review to investigate the diagnostic accuracy of the brain SPECT in (1) distinguishing FTD from AD and other dementias and (2) differentiating FTD variants. RESULTS: Overall, 391 studies were retrieved on the initial search and 35 studies composed the final selection, comprising a total number of 3142 participants of which 1029 had FTD. The sensitivity and the specificity for the differential diagnosis of FTD versus AD ranged from 56% to 88% and from 51% to 93%, respectively. SPECT is not superior to the clinical method of diagnosis, but the combination of SPECT with clinical data seems to improve the diagnostic accuracy. CONCLUSION: Brain perfusion SPECT has a limited value in the diagnostic framework of FTD. SPECT can be performed when FDG-PET is not available. SPECT is recommended only for selected cases when the diagnosis is challenging using conventional methods.

Longitudinal volumetric changes in amygdala subregions in frontotemporal dementia.

Amygdala atrophy has been found in frontotemporal dementia (FTD), yet the specific changes of its subregions across different FTD phenotypes remain unclear. The aim of this study was to investigate the volumetric alterations of the amygdala subregions in FTD phenotypes and how they evolve with disease progression. Patients clinically diagnosed with behavioral variant FTD (bvFTD) (n = 20), semantic dementia (SD) (n = 20), primary nonfluent aphasia (PNFA) (n = 20), Alzheimer's disease (AD) (n = 20), and 20 matched healthy controls underwent whole brain structural MRI. The patient groups were followed up annually for up to 3.5 years. Amygdala nuclei were segmented using FreeSurfer, corrected by total intracranial volumes, and grouped into the basolateral, superficial, and centromedial subregions. Linear mixed effects models were applied to identify changes in amygdala subregional volumes over time. At baseline, bvFTD, SD, and AD displayed global amygdala volume reduction, whereas amygdala volume appeared to be preserved in PNFA. Asymmetrical amygdala atrophy (left > right) was most pronounced in SD. Longitudinally, SD and PNFA showed greater rates of annual decline in the right basolateral and superficial subregions compared to bvFTD and AD. The findings provide comprehensive insights into the differential impact of FTD pathology on amygdala subregions, revealing distinct atrophy patterns that evolve over disease progression. The characterization of amygdala subregional involvement in FTD and their potential role as biomarkers carry substantial clinical implications.

Prevalence, treatment, and neural correlates of apathy in different forms of dementia: a narrative review.

OBJECTIVES: The aim of this review is to provide an overview on prevalence and clinical tools for the diagnosis of apathy, as well as on neurophysiological and neuroimaging findings obtained from studies in patients with apathy in different forms of dementia, including Alzheimer's disease (AD), vascular (VaD) and mixed dementia, frontotemporal dementia (FTD), and Parkinson's disease dementia (PDD). METHODS: Randomized controlled trials, non-randomized controlled trials, controlled before-after studies, and interrupted time series from four databases (WebOfScience, Scopus, Pubmed, and PsycINFO) addressing apathy in adults or older people aged over 65 years of age affected by dementia were included. RESULTS: The prevalence of apathy was 26-82% for AD, 28.6-91.7 for VaD, 29-97.5% in PDD, and 54.8-88.0 in FTD. The assessment of apathy was not consistent in the reviewed studies. Methylphenidate was the most successful pharmacological treatment for apathy. Neurobiological studies highlighted the relationship between both structural and functional brain areas and the presence or severity of apathy. CONCLUSION: Apathy is a very common disorder in all types of dementia, although it is often underdiagnosed and undertreated. Further studies are needed to investigate its diagnosis and management. A consensus on the different evaluation scales should be achieved.

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC. METHODS: We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity. RESULTS: We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability. CONCLUSION: We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.

Behavioral Variant Frontotemporal Dementia With the Dominantly Affected Caudate Nucleus in 18 F-FP-CIT PET/CT.

ABSTRACT: Frontotemporal dementia is a clinical syndrome that is characterized by a progressive deterioration in behavior, personality, and/or language, with relative preservation of memory, and its phenotype and molecular basis are heterogeneous. We present a case of a 62-year-old female patient who underwent 18 F-FDG PET/CT and 18 F-FP-CIT PET/CT for differential diagnosis of psychiatric disease and types of dementia. 18 F-FDG PET/CT image showed a compatible finding for frontotemporal dementia, and 18 F-FP-CIT PET/CT image showed dominantly decreased dopamine transporter activity in the bilateral caudate nucleus.

Digital markers of motor speech impairments in spontaneous speech of patients with ALS-FTD spectrum disorders.

OBJECTIVE: To evaluate automated digital speech measures, derived from spontaneous speech (picture descriptions), in assessing bulbar motor impairments in patients with ALS-FTD spectrum disorders (ALS-FTSD). METHODS: Automated vowel algorithms were employed to extract two vowel acoustic measures: vowel space area (VSA), and mean second formant slope (F2 slope). Vowel measures were compared between ALS with and without clinical bulbar symptoms (ALS + bulbar (n = 49, ALSFRS-r bulbar subscore: x¯ = 9.8 (SD = 1.7)) vs. ALS-nonbulbar (n = 23), behavioral variant frontotemporal dementia (bvFTD, n = 25) without a motor syndrome, and healthy controls (HC, n = 32). Correlations with bulbar motor clinical scales, perceived listener effort, and MRI cortical thickness of the orobuccal primary motor cortex (oral PMC) were examined. We compared vowel measures to speaking rate, a conventional metric for assessing bulbar dysfunction. RESULTS: ALS + bulbar had significantly reduced VSA and F2 slope than ALS-nonbulbar (|d|=0.94 and |d|=1.04, respectively), bvFTD (|d|=0.89 and |d|=1.47), and HC (|d|=0.73 and |d|=0.99). These reductions correlated with worse bulbar clinical scores (VSA: R = 0.33, p = 0.043; F2 slope: R = 0.38, p = 0.011), greater listener effort (VSA: R=-0.43, p = 0.041; F2 slope: p > 0.05), and cortical thinning in oral PMC (F2 slope: ß = 0.0026, p = 0.017). Vowel measures demonstrated greater sensitivity and specificity for bulbar impairment than speaking rate, while showing independence from cognitive and respiratory impairments. CONCLUSION: Automatic vowel measures are easily derived from a brief spontaneous speech sample, are sensitive to mild-moderate stage of bulbar disease in ALS-FTSD, and may present better sensitivity to bulbar impairment compared to traditional assessments such as speaking rate.

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.

Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia.

Alzheimer's disease and Frontotemporal dementia are common forms of neurodegenerative dementia. Behavioral alterations and cognitive impairments are found in the clinical courses of both diseases, and their differential diagnosis can sometimes pose challenges for physicians. Therefore, an accurate tool dedicated to this diagnostic challenge can be valuable in clinical practice. However, current structural imaging methods mainly focus on the detection of each disease but rarely on their differential diagnosis. In this paper, we propose a deep learning-based approach for both disease detection and differential diagnosis. We suggest utilizing two types of biomarkers for this application: structure grading and structure atrophy. First, we propose to train a large ensemble of 3D U-Nets to locally determine the anatomical patterns of healthy people, patients with Alzheimer's disease and patients with Frontotemporal dementia using structural MRI as input. The output of the ensemble is a 2-channel disease's coordinate map, which can be transformed into a 3D grading map that is easily interpretable for clinicians. This 2-channel disease's coordinate map is coupled with a multi-layer perceptron classifier for different classification tasks. Second, we propose to combine our deep learning framework with a traditional machine learning strategy based on volume to improve the model discriminative capacity and robustness. After both cross-validation and external validation, our experiments, based on 3319 MRIs, demonstrated that our method produces competitive results compared to state-of-the-art methods for both disease detection and differential diagnosis.

Frontotemporal Dementia-Parkinsonism Due to MAPT Gene Variant Presenting with Rest and Action Tremor.

A 50-year-old male presented with a four-year history of gradually progressive rest tremor in the distal right lower limb and then spreading to the left lower limb in last 10-12 months. He developed right arm rest and action tremor two years later. Magnetic resonance imaging scans showed progressive frontotemporal and asymmetrical mesial temporal atrophy. Genetic testing revealed a heterozygous c.915+16C>T pathogenic variant in intron 9 of the MAPT gene. Presentation with rest tremor should not exclude frontotemporal dementia-parkinsonism due to a MAPT variant as a differential diagnosis though rest tremor is a rare presentation.

Coexistence of Amyotrophic Lateral Sclerosis and Alzheimer's Disease: Case Report and Review of the Literature.

We describe a case of amyotrophic lateral sclerosis (ALS) associated with Alzheimer's disease (AD) and review the literature about the coexistence of the two entities, highlighting the following: mean age at onset is 63.8 years, with slight female predominance; ALS tends to manifest after cognitive impairment and often begins in the bulbar region; average disease duration is 3 years; cognitive phenotype is mostly amnestic; the pattern of brain involvement is, in most cases, consistent with AD. Our case and the reviewed ones suggest that patients with ALS and dementia lacking unequivocal features of FTD should undergo additional examinations in order to recognize AD.