Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI.

BACKGROUND: The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI). METHODS: We analyzed data from 1,617 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques. RESULTS: Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models' ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures. CONCLUSION: This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.

Nisin Inhibition of Gram-Negative Bacteria.

Aims: This study investigates the activity of the broad-spectrum bacteriocin nisin against a large panel of Gram-negative bacterial isolates, including relevant plant, animal, and human pathogens. The aim is to generate supportive evidence towards the use/inclusion of bacteriocin-based therapeutics and open avenues for their continued development. Methods and Results: Nisin inhibitory activity was screened against a panel of 575 strains of Gram-negative bacteria, encompassing 17 genera. Nisin inhibition was observed in 309 out of 575 strains, challenging the prevailing belief that nisin lacks effectiveness against Gram-negative bacteria. The genera Acinetobacter, Helicobacter, Erwinia, and Xanthomonas exhibited particularly high nisin sensitivity. Conclusions: The findings of this study highlight the promising potential of nisin as a therapeutic agent for several key Gram-negative plant, animal, and human pathogens. These results challenge the prevailing notion that nisin is less effective or ineffective against Gram-negative pathogens when compared to Gram-positive pathogens and support future pursuits of nisin as a complementary therapy to existing antibiotics. Significance and Impact of Study: This research supports further exploration of nisin as a promising therapeutic agent for numerous human, animal, and plant health applications, offering a complementary tool for infection control in the face of multidrug-resistant bacteria.

Genetic-based patient stratification in Alzheimer's disease.

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

Control de diabetes y consultas al médico de familia durante la pandemia de COVID-19: estudio multicéntrico en atención primaria / Diabetes control and visits to the family doctor during the COVID-19 pandemic: a multicenter study in primary care

Objetivos: Determinar los cambios de frecuentación de consultas presenciales (CP) y telemáticas (CT) a su médico de familia en pacientes con diabetes tipo 2 (DM2) durante la pandemia de COVID-19 y su relación con el control de su enfermedad. Diseño: Estudio multicéntrico de seguimiento retrospectivo. Emplazamiento: Siete centros de salud en Tenerife, España. Participantes: Un total de 3.543 pacientes con DM2. Mediciones: Sexo, edad, CP, CT y control de DM2 mediante hemoglobina glicosilada (A1c) durante el periodo 2019-2021. Se ajustaron modelos de regresión logística con el control de DM2 como efecto, y con las demás mediciones como variables independientes. Resultados: El 50% eran mujeres. El 38% tenía 65 años o menos. Se midió la A1c al 84% de los pacientes en 2019, 68% en 2020, y 77% en 2021. Presentaron buen control el 58,4% en 2019, 46,1% en 2020 y 50,3% en 2021. Las CP fueron 7 en 2019, 4 en 2020 y 5 en 2021 (p<0,001). Las razones de ventaja (IC95%) de buen control en 2019 fueron 1,04 (1,04-1,05) por cada año más de edad y 1,03 (1,01-1,04) por cada CP más; en 2020 fueron 1,04 (1,03-1,05) por cada año más de edad, 1,05 (1,04-1,07) por cada CP más y 1,04 (1,02-1,07) por cada CT más; en 2021 fueron 1,04 (1,04-1,05) por cada año más de edad, 1,05 (1,03-1,06) por cada CP más y 1,02 (1,00-1,04) por cada CT más. Conclusiones: El control de pacientes con DM2 durante 2019-2021 tuvo una relación directa con el cambio de frecuentación al centro de salud, con diferencias según el tipo de consulta y la edad.(AU)

Objectives: To determine whether in patients with type 2 diabetes (DM2) the changes in their relationship with family doctors during the COVID-19 pandemic, in-person (iPC) and telematic (TC) consultations, were associated with control of their disease. Design: Multicentric study of retrospective follow-up. Setting: Seven health centers in Tenerife, Spain. Participants: 3543 patients with DM2. Main measurements: Sex, age, iPC, TC and DM2 control using glycosylated hemoglobin (A1c) during the period 2019-2021. Logistic regression models were fitted with DM2 control as an effect, and with the other measurements as independent variables. Results: 50% were women. 38% were less than 65 years old. A1c was measured in 84% of patients in 2019, 68% in 2020, and 77% in 2021. 58.4% had good control in 2019, 46.1% in 2020, and 50.3% in 2021. Median iPC were 7 in 2019, 4 in 2020 and 5 in 2021 (p<0.001). The OR(95%CI) of good control in 2019 were 1.04(1.04-1.05) per year of age and 1.03(1.01-1.04) for each iPC; In 2020 they were 1.04 (1.03-1.05) per year of age, 1.05 (1.04-1.07) for each iPC and 1.04 (1.02-1.07) for each TC; in 2021 they were 1.04 (1.04-1.05) per year of age, 1.05 (1.03-1.06) for each iPC and 1.02 (1.00-1.04) for each TC. Conclusions: The control of patients with DM2 during the period 2019-2021 had a direct relationship with the change in the frequency of consultations at the health center, with differences depending on the type of consultation and the age of the patient.(AU)

ORAI1 inhibition as an efficient preclinical therapy for tubular aggregate myopathy and Stormorken syndrome.

Tubular aggregate myopathy (TAM) and Stormorken syndrome (STRMK) are clinically overlapping disorders characterized by childhood-onset muscle weakness and a variable occurrence of multisystemic signs, including short stature, thrombocytopenia, and hyposplenism. TAM/STRMK is caused by gain-of-function mutations in the Ca2+ sensor STIM1 or the Ca2+ channel ORAI1, both of which regulate Ca2+ homeostasis through the ubiquitous store-operated Ca2+ entry (SOCE) mechanism. Functional experiments in cells have demonstrated that the TAM/STRMK mutations induce SOCE overactivation, resulting in excessive influx of extracellular Ca2+. There is currently no treatment for TAM/STRMK, but SOCE is amenable to manipulation. Here, we crossed Stim1R304W/+ mice harboring the most common TAM/STRMK mutation with Orai1R93W/+ mice carrying an ORAI1 mutation partially obstructing Ca2+ influx. Compared with Stim1R304W/+ littermates, Stim1R304W/+Orai1R93W/+ offspring showed a normalization of bone architecture, spleen histology, and muscle morphology; an increase of thrombocytes; and improved muscle contraction and relaxation kinetics. Accordingly, comparative RNA-Seq detected more than 1,200 dysregulated genes in Stim1R304W/+ muscle and revealed a major restoration of gene expression in Stim1R304W/+Orai1R93W/+ mice. Altogether, we provide physiological, morphological, functional, and molecular data highlighting the therapeutic potential of ORAI1 inhibition to rescue the multisystemic TAM/STRMK signs, and we identified myostatin as a promising biomarker for TAM/STRMK in humans and mice.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers.

AIMS: The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values. METHODS: We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aß(1-42), Aß(1-42)/Aß(1-40) ratio, tTau, and pTau. RESULTS: The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia. CONCLUSION: We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

[Diabetes control and visits to the family doctor during the COVID-19 pandemic: a multicenter study in primary care]. / Control de diabetes y consultas al médico de familia durante la pandemia de COVID-19: estudio multicéntrico en atención primaria.

OBJECTIVES: To determine whether in patients with type 2 diabetes (DM2) the changes in their relationship with family doctors during the COVID-19 pandemic, in-person (iPC) and telematic (TC) consultations, were associated with control of their disease. DESIGN: Multicentric study of retrospective follow-up. SETTING: Seven health centers in Tenerife, Spain. PARTICIPANTS: 3543 patients with DM2. MAIN MEASUREMENTS: Sex, age, iPC, TC and DM2 control using glycosylated hemoglobin (A1c) during the period 2019-2021. Logistic regression models were fitted with DM2 control as an effect, and with the other measurements as independent variables. RESULTS: 50% were women. 38% were less than 65 years old. A1c was measured in 84% of patients in 2019, 68% in 2020, and 77% in 2021. 58.4% had good control in 2019, 46.1% in 2020, and 50.3% in 2021. Median iPC were 7 in 2019, 4 in 2020 and 5 in 2021 (p<0.001). The OR(95%CI) of good control in 2019 were 1.04(1.04-1.05) per year of age and 1.03(1.01-1.04) for each iPC; In 2020 they were 1.04 (1.03-1.05) per year of age, 1.05 (1.04-1.07) for each iPC and 1.04 (1.02-1.07) for each TC; in 2021 they were 1.04 (1.04-1.05) per year of age, 1.05 (1.03-1.06) for each iPC and 1.02 (1.00-1.04) for each TC. CONCLUSIONS: The control of patients with DM2 during the period 2019-2021 had a direct relationship with the change in the frequency of consultations at the health center, with differences depending on the type of consultation and the age of the patient.

Dating Violence and Mental Health in Emerging Adulthood.

Dating violence constitutes a serious social and health problem. This study aims to increase knowledge on dating violence in emerging adulthood by analysing the relevance of gender and of having or not having a current partner in the victimization and perpetration of such violence. It also analyses the association between dating violence and mental health, as well as the relevance of traditional gender role attitudes and the internalization of feminine/expressive and masculine/instrumental traits in the victimization and perpetration of such types of violence. The participants were 930 Spanish emerging adults who were assessed by six self-report questionnaires and scales. Men reported more psychological and physical violence victimization and physical violence perpetration than women, and women and men without a current partner reported more psychological and sexual violence than women and men with a current partner. Dating violence victimization was associated with more mental symptomatology, less life satisfaction, and lower self-esteem in men with a current partner and in women without a current partner. The main predictor of dating violence victimization was dating violence perpetration, and the main predictor of dating violence perpetration was victimization by such violence. More traditional gender role attitudes also predicted greater victimization and perpetration of dating violence, except among women without a current partner.

Estrés relacionado con el trabajo y sintomatología mental en personas trabajadoras: Un análisis de género / Work-related stress and mental symptoms in working people: A gender analysis

El objetivo fue analizar el estrés laboral de mujeres y hombres con empleo y categorías laborales y demográficas similares, estudiando su asociación con la sintomatología mental. Estudio exploratoriodescriptivo y transversal con una muestra de conveniencia formada por 2643 personas con empleo y edades entre 18 y 64 años de las cuales el 54.3% son hombres y el 45.7% mujeres. Todas fueron evaluadas mediante cinco autoinformes y una hoja de recogida de datos sociodemográficos y de usos del tiempo. Resultados: El 67.5% de los hombres y el 66.5% de las mujeres tuvo algún tipo de estrés relacionado con el trabajo, no existiendo diferencias entre mujeres y hombres en ninguna de las medidas de estrés laboral, en insatisfacción con el rol laboral, ni en la asociación entre el estrés laboral y la sintomatología mental, asociación que era muy baja en mujeres y en hombres. Las mujeres tenían más estrés crónico no laboral y mayores contrariedades diarias que los hombres, además de mayor sintomatología mental de ansiedad, depresión grave, somática y de disfunción social. Asimismo, dedicaban más tiempo a las tareas domésticas y de cuidado y menos al ocio y a las actividades físico-deportivas que los hombres. Conclusiones: los resultados evidenciaron que el rol laboral no supone amenazas específicas para la salud mental de las mujeres con empleo, aunque sí parecen suponerlas su mayor dedicación a las tareas domésticas y de cuidado. Los resultados del presente trabajo son relevantes para el diseño de políticas y programas destinados a fomentar la salud de la ciudadanía y al logro de mayor igualdad de género(AU)

The objective was to analyze work stress in women and men with similar occupation and demographic categories, studying its association with mental symptomatology. Exploratory-descriptive and cross-sectional study with a convenience sample of 2643 employed persons aged 18 to 64 years, 54.3% of whom were men and 45.7% women. All were assessed by five self-reports and a sociodemographic and time-use data collection sheet. Results: 67.5% of the men and 66.5% of the women had some type of work-related stress. There were no differences between women and men in any of the measures of work stress, in dissatisfaction with the work role, or in the association between work stress and mental symptomatology, association that was very low in both women and men. Women had more chronic non-work stress and greater daily hassles than men, as well as greater symptomatology of anxiety, severe depression, somatic and social dysfunction. In addition, they spent more time on housework and caregiving and less time on leisure and physical-sports activities than men. Conclusions: the results show that the work role does not pose specific threats to the mental health of working women, although their greater dedication to domestic and caregiving tasks does seem to do so. The results of this study are relevant for the design of policies and programs aimed at promoting the health of citizens and achieving greater gender equality(AU)

Gender, life events, and mental well-being in emerging adulthood.

BACKGROUND: Emerging adulthood is a critical period of life involving many life transitions that may generate stress and compromise health and mental well-being. AIMS: To know the most frequent life events of women and men in emerging adulthood, analyzing also the relevance that such stressors have on their psychological well-being and life satisfaction. A second aim is to determine the relevance of age, educational level, most frequent life events, coping styles, and perceived social support as risk and protective factors for well-being. METHOD: The sample consisted of 2,000 individuals from the general Spanish population (55% women), aged between 18 and 29, who were assessed using five questionnaires and scales measuring life events, coping styles, life satisfaction, psychological well-being, and social support. RESULTS: The findings showed that 90% of the sample had experienced one or more life events during the previous year and that a higher number of life events experienced over the past year was associated with lower mental well-being. Multiple regression analyses made clear that, although some events experienced in the previous year (namely, family conflicts and change in the relationship with parents) were associated with lower women's and men's well-being, the most important determinants of well-being in either gender were coping styles; however, some predictors of women's well-being proved different from those of men. In the case of women, family and intimate partner conflicts predicted lower life satisfaction and psychological well-being was lower in the case of family conflicts. As for men, work or academic life events predicted lower life satisfaction. CONCLUSION: These research findings are relevant for the design of programs and strategies to improve mental well-being in emerging adulthood.

Multi-Disease Validation of the RUDAS for Cognitive Screening in Alzheimer's Disease, Parkinson's Disease, and Multiple Sclerosis.

BACKGROUND: The Rowland Universal Dementia Assessment Scale (RUDAS) is a cognitive test with favorable diagnostic properties for detecting dementia and a low influence of education and cultural biases. OBJECTIVE: We aimed to validate the RUDAS in people with Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS). METHODS: We enrolled one hundred and fifty participants (60 with AD, 30 with PD, 60 with MS, and 120 healthy controls (HC)). All clinical groups completed a comprehensive neuropsychological battery, RUDAS, and standard cognitive tests of each disorder: MMSE, SCOPA-COG, and Symbol Digit Modalities Test. Intergroup comparisons between clinical groups and HC and ROC curves were estimated. Random Forest algorithms were trained and validated to detect cognitive impairment using RUDAS and rank the most relevant scores. RESULTS: The RUDAS scores were lower in patients with AD, and patients with PD and MS showed cognitive impairment compared to healthy controls. Effect sizes were generally large. The total score was the most discriminative, followed by the memory score. Correlations with standardized neuropsychological tests were moderate to high. Random Forest algorithms obtained accuracies over 80-90% using the RUDAS for diagnosing AD and cognitive impairment associated with PD and MS. CONCLUSION: Our results suggest the RUDAS is a valid test candidate for multi-disease cognitive screening tool in AD, PD, and MS.

Partial Thrombosis of Corpus Cavernosum.

Thrombosis of the corpus cavernosum is a rare disease of unknown cause that usually affects young men. We present the case of a 25-year-old man with an unilateral, painful perineal mass and ultrasound scan compatible with this entity. The magnetic resonance and tomography computarised scan images corroborate the diagnosis. Anticoagulants were prescribed which solved the clinical picture.

On the limits of graph neural networks for the early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease whose molecular mechanisms are activated several years before cognitive symptoms appear. Genotype-based prediction of the phenotype is thus a key challenge for the early diagnosis of AD. Machine learning techniques that have been proposed to address this challenge do not consider known biological interactions between the genes used as input features, thus neglecting important information about the disease mechanisms at play. To mitigate this, we first extracted AD subnetworks from several protein-protein interaction (PPI) databases and labeled these with genotype information (number of missense variants) to make them patient-specific. Next, we trained Graph Neural Networks (GNNs) on the patient-specific networks for phenotype prediction. We tested different PPI databases and compared the performance of the GNN models to baseline models using classical machine learning techniques, as well as randomized networks and input datasets. The overall results showed that GNNs could not outperform a baseline predictor only using the APOE gene, suggesting that missense variants are not sufficient to explain disease risk beyond the APOE status. Nevertheless, our results show that GNNs outperformed other machine learning techniques and that protein-protein interactions lead to superior results compared to randomized networks. These findings highlight that gene interactions are a valuable source of information in predicting disease status.

Partial Thrombosis of Corpus Cavernosum

Thrombosis of the corpus cavernosum is a rare disease of unknown cause that usually affects young men. We present the case of a 25-year-old man with an unilateral, painful perineal mass and ultrasound scan compatible with this entity. The magnetic resonance and tomography computarised scan images corroborate the diagnosis. Anticoagulants were prescribed which solved the clinical picture (AU)

Validation of a brief cross-cultural cognitive screening test in Multiple Sclerosis.

BACKGROUND: Several batteries have been developed for the cognitive assessment of people with multiple sclerosis (MS). However, all these tests have some limitations in general clinical practice and from a cross-cultural perspective. In this study, we aimed to validate a novel cognitive screening test, the Cross-Cultural Dementia screening test (CCD), in pwMS. METHODS: Seventy-five participants with relapsing-remitting MS and 75 healthy controls were enrolled and completed a comprehensive neuropsychological battery and the CCD. Intergroup comparisons, effect sizes, and correlations with previously validated tests were calculated for a majority and a pilot study of a minority sample. ROC curves were estimated, and random forest classification models were developed. RESULTS: There were statistically significant differences between cognitively impaired MS (MS-CI) group and healthy controls, and between MS-CI and non-cognitively impaired MS group in all subtests of CCD with medium to large effect sizes. Correlations with standardized neuropsychological tests were moderate to high, supporting concurrent validity. These results were replicated in the minority sample. The random forest models showed a very accurate classification using the CCD. This test showed good psychometric properties compared with SDMT. CONCLUSIONS: Our study validates the CCD for cognitive impairment screening in MS, showing advantages over other routinely used cognitive tests.

Variant rs4149584 (R92Q) of the TNFRSF1A gene in patients with familial multiple sclerosis.

INTRODUCTION: Genomic studies have identified numerous genetic variants associated with susceptibility to multiple sclerosis (MS); however, each one explains only a small percentage of the risk of developing the disease. These variants are located in genes involved in specific pathways, which supports the hypothesis that the risk of developing MS may be linked to alterations in these pathways, rather than in specific genes. We analyzed the role of the TNFRSF1A gene, which encodes one of the TNF-α receptors involved in a signaling pathway previously linked to autoimmune disease. METHODS: We included 138 individuals from 23 families including at least 2 members with MS, and analyzed the presence of exonic variants of TNFRSF1A through whole-exome sequencing. We also conducted a functional study to analyze the pathogenic mechanism of variant rs4149584 (-g.6442643C > G, NM_001065.4:c.362 G > A, R92Q) by plasmid transfection into human oligodendroglioma (HOG) cells, which behave like oligodendrocyte lineage cells; protein labeling was used to locate the protein within cells. We also analyzed the ability of transfected HOG cells to proliferate and differentiate into oligodendrocytes. RESULTS: Variant rs4149584 was found in 2 patients with MS (3.85%), one patient with another autoimmune disease (7.6%), and in 5 unaffected individuals (7.46%). The 2 patients with MS and variant rs4149584 were homozygous carriers and belonged to the same family, whereas the remaining individuals presented the variant in heterozygosis. The study of HOG cells transfected with the mutation showed that the protein does not reach the cell membrane, but rather accumulates in the cytoplasm, particularly in the endoplasmic reticulum and near the nucleus; this suggests that, in the cells presenting the mutation, TNFRSF1 does not act as a transmembrane protein, which may alter its signaling pathway. The study of cell proliferation and differentiation found that transfected cells continue to be able to differentiate into oligodendrocytes and are probably still capable of producing myelin, although they present a lower rate of proliferation than wild-type cells. CONCLUSIONS: Variant rs4149584 is associated with risk of developing MS. We analyzed its functional role in oligodendrocyte lineage cells and found an association with MS in homozygous carriers. However, the associated molecular alterations do not influence the differentiation into oligodendrocytes; we were therefore unable to confirm whether this variant alone is pathogenic in MS, at least in heterozygosis.

Identification of the main components of spontaneous speech in primary progressive aphasia and their neural underpinnings using multimodal MRI and FDG-PET imaging.

BACKGROUND: Primary progressive aphasia (PPA) is a clinical syndrome characterized by gradual loss of language skills. This study aimed to evaluate the diagnostic capacity of a connected speech task for the diagnosis of PPA and its variants, to determine the main components of spontaneous speech, and to examine their neural correlates. METHODS: A total of 118 participants (31 patients with nfvPPA, 11 with svPPA, 45 with lvPPA, and 31 healthy controls) were evaluated with the Cookie Theft picture description task and a comprehensive language assessment protocol. Patients also underwent 18F-fluorodeoxyglucose positron emission tomography and magnetic resonance imaging studies. Principal component analysis and machine learning were used to evaluate the main components of connected speech and the accuracy of connected speech parameters for diagnosing PPA. Voxel-based analyses were conducted to evaluate the correlation between spontaneous speech components and brain metabolism, brain volumes, and white matter microstructure. RESULTS: Discrimination between patients with PPA and controls was 91.67%, with 77.78% discrimination between PPA variants. Parameters related to speech rate and lexical variables were the most discriminative for classification. Three main components were identified: lexical features, fluency, and syntax. The lexical component was associated with ventrolateral frontal regions, while the fluency component was associated with the medial superior prefrontal cortex. Number of pauses was more related with the left parietotemporal region, while pauses duration with the bilateral frontal lobe. The lexical component was correlated with several tracts in the language network (left frontal aslant tract, left superior longitudinal fasciculus I, II, and III, left arcuate fasciculus, and left uncinate fasciculus), and fluency was linked to the frontal aslant tract. CONCLUSION: Spontaneous speech assessment is a useful, brief approach for the diagnosis of PPA and its variants. Neuroimaging correlates suggested a subspecialization within the left frontal lobe, with ventrolateral regions being more associated with lexical production and the medial superior prefrontal cortex with speech rate.