Demencia frontotemporal variante conductual: aproximación clínica y terapéutica

Design and Verbal Fluency in Alzheimer's Disease and Frontotemporal Dementia: Clinical and Metabolic Correlates

OBJECTIVE

Cognitive processes underlying verbal and design fluency, and their neural correlates in patients with Alzheimer's disease (AD) and behavioural variant Frontotemporal Dementia (bvFTD) remain unclear. We hypothesised that verbal and design fluency may be associated with distinct neuropsychological processes in AD and FTD, showing different patterns of impairment and neural basis.

METHODS

We enrolled 142 participants including patients with AD (n = 80, mean age = 74.71), bvFTD (n = 34, mean age = 68.18), and healthy controls (HCs) (n = 28, mean age = 71.14), that underwent cognitive assessment and ¹⁸F-fluorodeoxyglucose positron emission tomography imaging.

RESULTS

Semantic and phonemic fluency showed the largest effect sizes between groups, showing lower scores in bvFTD than AD and HCs, and lower scores in AD than HC. Both AD and bvFTD showed a lower number of unique designs in design fluency in comparison to HC. Semantic fluency was correlated with left frontotemporal lobe in AD, and with left frontal, caudate, and thalamus in bvFTD. Percentage of unique designs in design fluency was associated with the metabolism of the bilateral fronto-temporo-parietal cortex in AD, and the bilateral frontal cortex with right predominance in bvFTD. Repetitions in AD were correlated with bilateral frontal, temporal, and parietal lobes, and with left prefrontal cortex in bvFTD.

CONCLUSIONS

Our findings demonstrate differential underlying cognitive processes in verbal and design fluency in AD and bvFTD. While memory and executive functioning associated with fronto-temporo-parietal regions were key in AD, attention and executive functions correlated with the frontal cortex and played a more significant role in bvFTD during fluency tasks.

GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer’s disease and frontotemporal dementia using genetic algorithms

Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients’ evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artificial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer’s disease (AD) and frontotemporal dementia (FTD).

Graphical abstract

Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism.

Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms

BACKGROUND

Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning-based models for the diagnosis using cognitive tests.

METHODS

Three hundred and twenty-nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono-objective and multi-objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta-model strategy.

RESULTS

Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests.

CONCLUSIONS

Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.

Inhibition impairment in frontotemporal dementia, amyotrophic lateral sclerosis, and Alzheimer's disease: clinical assessment and metabolic correlates

The ability to reject an automatic tendency, i.e. inhibition, has been linked to the prefrontal cortex, but its neural underpinnings are still controversial. Neurodegenerative diseases represent an interesting model to explore this issue, given its frequent impairment in these disorders. We investigated the inhibitory impairment and its neural basis using four different tests, which evaluate the presence of inhibitory dysfunction (Stroop test, Hayling test, and two graphical perseveration tests), and assessed their correlation with brain metabolism using ¹⁸F-fluorodeoxyglucose positron emission tomography in a group of 76 participants with behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS) and healthy controls (HC). Inhibition impairment was more frequent in bvFTD and AD, than ALS and HC. AD and bvFTD only differed in the strategy used in Hayling test, and the frequency of impairment in graphical perseveration tests. Correlation between inhibition tests was moderate. The Stroop test correlated with several regions of the frontal and parietal lobes, mainly on the left side. Hayling test correlated with almost all regions of the frontal lobe and, especially, with the orbitofrontal cortex. Some differences in the impaired regions in each disease were found. Inhibition ability was mainly impaired in bvFTD and AD, and it correlated with the bilateral frontal lobe metabolism. There were certain particularities according to the specific task and patients evaluated. These dissimilarities may support the concept of inhibition as a multidimensional construct, with the involvement of common and divergent neural mechanisms.

Episodic Memory Dysfunction in Behavioral Variant Frontotemporal Dementia: A Clinical And FDG-PET Study

BACKGROUND

Episodic memory disturbance is still considered as an exclusion criterion for behavioral variant frontotemporal dementia (bvFTD), but growing evidence suggests that memory can be impaired.

OBJECTIVE

Our main purposes were to assess episodic memory in a group of bvFTD patients comparatively with Alzheimer's disease (AD) patients, and analyze the relationship between episodic memory and brain metabolism measured using positron emission tomography imaging with 18F-fluorodeoxyglucose (FDG-PET).

METHODS

Twenty-six bvFTD, 29 AD, and 24 healthy controls were included. Episodic memory was assessed by the Free and Cued Selective Reminding Test (FCSRT), which controls for effective encoding and measures memory consolidation processing. All participants underwent FDG-PET brain scans to provide data for voxel-based brain mapping analysis.

RESULTS

Half of bvFTD patients had a deficit of total, free delayed, and total free delayed recall as severe as AD patients (amnestic-FTD). The other half had FCSRT scores similar to controls (non-amnestic-FTD). Imaging analyses revealed that amnestic-FTD showed bilateral lower metabolism than non-amnestic-FTD in anterior parahippocampal and inferior temporal gyri. Additionally, FCSRT total and total delayed scores were inversely correlated with parahippocampal metabolism in both bvFTD and AD. Besides, bvFTD showed an inverse association among FCSRT and inferior temporal metabolism.

CONCLUSIONS

Our findings support that bvFTD could present a genuine amnesia affecting storage and consolidation abilities, which involves structures implicated in the Papez circuit, as occurs in AD, and also inferior temporal regions. These results contribute to understanding the mechanisms underpinning memory dysfunction in bvFTD, and may be relevant to further revisions of the current diagnostic criteria.

A Case of Fronto-Temporal Dementia (FTD) Masquerading as Mood Disorder

A 56-year old Chinese female was referred to an academic medical center with atypical, treatment-resistant depression that continued for approximately 3 years after her sister's death. Comprehensive evaluation including neurocognitive testing, EEG, spinal tap, HIV testing and brain MRI revealed behavioral variant of fronto-temporal dementia (bvFTD) with significant frontal and temporal lobe atrophy.This patient's unusual clinical presentation emphasizes the overlap between depression and bvFTD, and underlines the importance of prompt, accurate diagnosis to minimize often-ineffective pharmacological interventions and caregiver burnout.

Validation of the Lille's Apathy Rating Scale in Very Mild to Moderate Dementia

OBJECTIVE

Apathy is one of the most common and disabling syndromes of dementia and presents at all stages of the disease. Comprehensive and structured methods to assess apathy in dementia are still needed. Lille's Apathy Rating Scale (LARS) has shown good psychometric properties for apathy evaluation in Parkinson disease but has not been validated in dementia. The aim of this study was to validate the LARS in a cohort of patients with very mild to moderate dementia.

METHODS

101 patients with cognitive impairment (Clinical Dementia Rating ≤ 2) and 50 healthy subjects were recruited. Patient diagnoses included 43 individuals with Alzheimer disease, 41 frontotemporal dementia, and 17 primary progressive aphasia. In addition to LARS, the following assessments were administered: Clinical Dementia Rating, Interview for Deterioration in Daily Living Activities in Dementia, Functional Activities Questionnaire, Frontal Behavioral Inventory, Neuropsychiatric Inventory (NPI), and Hamilton Depression Rating Scale.

RESULTS

Internal consistency for LARS (Cronbach's alpha) was 0.940. Test-retest intraclass correlation coefficient (ICC) was 0.940 and inter-rater ICC was 0.987. The correlation among LARS and NPI apathy scores (concurrent validity) was 0.834. Receiver operating characteristic analysis estimated an area under the curve of 0.987. The optimal cutoff point was -10. Although total LARS score was influenced by the presence of depression, this disorder was independent with respect to apathy.

CONCLUSION

LARS is reliable and valid for detecting and quantifying apathy in patients with dementia, even in very early stages of the disease.