Beatriz Bosch

Tomatoes are the major dietary source of lycopene, a carotenoid that crosses the blood–brain barrier and exerts antioxidant and anti-inflammatory effects. However, the impact of tomato consumption on cognitive function in healthy adults remains unclear. This study assessed the effects of concentrated tomato paste on cognitive performance and explored potential mechanisms, including brain-derived neurotrophic factor (BDNF) and functional brain connectivity. A randomized, two-period crossover trial (ClinicalTrials.gov: NCT05891977) was conducted in 47 healthy adults aged 40–55 years assigned to two 3-month interventions separated by a 1-month washout: (a) daily consumption of concentrated tomato paste (0.5 g/kg body weight) and (b) a lycopene-restricted control diet. Cognitive performance was evaluated using validated neuropsychological tests (d2-R, Face-Name Associative Memory Exam, Modified Wisconsin Card Sorting Test), alongside plasma lycopene and BDNF, and resting-state functional magnetic resonance imaging (fMRI). Forty-two participants completed the study. Tomato intake improved selective attention (concentration performance: +7.2 points; processing speed: +8.3 points) and associative memory (face-name matching: +0.8 points). Plasma BDNF showed a borderline increase with tomato intake (mean difference 15.2 ng/mL). Resting-state fMRI revealed changes in brain networks, including reduced connectivity in frontoparietal and auditory networks, contrasting with reductions in the dorsal attention network during the control period. These findings provide evidence that tomato consumption may support cognitive function and modulate brain connectivity in healthy middle-aged adults.

Background: The application of blood‐based biomarkers for the identification of Alzheimer’s disease (AD) and the development of novel digital technologies as cognitive screening tests are critical to moving toward a reliable, more accessible early diagnosis. Our aim was to evaluate the diagnostic performance of a machine learning‐based cognitive assessment known as Altoida’s digital neuro‐signature (DNS) in patients with non‐degenerative mild cognitive impairment (ndMCI) and MCI due to AD (prodromal AD) and its association with CSF and plasma biomarkers.Method: Altoida’s MCI‐DNS is a 10‐minute cognitive test battery evaluating activities of daily living via motoric and augmented reality tasks. The test consists of placing and finding virtual objects in a real environment and its final score is obtained by weighting multi‐modal digital data features, such as hands’ micro‐movements, speed, reaction times, or navigation trajectories. We included 81 participants, classified according to their clinical status and CSF AD biomarker profile as: cognitively unimpaired controls, CTR (n = 10; age = 68.5±5.9; MMSE = 29.4±1.1), ndMCI (n = 25; age = 67.6±7.2; MMSE = 26.9±1.9) and prodromal AD (n = 46; age = 70.8±4.9; MMSE = 24.3±3.3). We further investigated a subsample of participants classified according to their plasma pTau₁₈₁ levels as measured by SiMoA [cutoff = 1.37 pg/mL (Sarto et al., 2022)]: pTau₁₈₁ negative (n = 27; age = 68.5±5.9; MMSE = 26.9±2.7) or pTau₁₈₁ positive (n = 30; age = 70.3±5.5; MMSE = 24.1±3.7).Result: Significant differences were found in MCI‐DNS scores between CTR group and ndMCI (F = 23.5; pF = 114.4; p42 negative and CSF Aβ₄₂ positive (F = 18.9; p181 negative and plasma pTau₁₈₁ positive (F = 6.16; p42, CSF Aβ₄₂/pTau ratio, CSF neurofilament‐light chain (NfL) and plasma pTau₁₈₁ concentrations (Fig. 3).Conclusion: Altoida’s MCI‐DNS test allows excellent discrimination between CTR and patients with MCI. MCI‐DNS scores significantly correlate with CSF AD core biomarkers, biomarkers of neurodegeneration and blood‐based biomarkers (i.e., plasma pTau₁₈₁).

Background: Plasma Neurofilament light chain (pNfL) is a promising biomarker to discriminate Frontotemporal dementia (FTD) from other diagnoses such as Alzheimer’s disease (AD) or psychiatric disorders. Currently, the diagnostic criteria for FTD syndromes are structured hierarchically into “Possible”, “Probable” and “Definite” levels depending on the degree of confidence set by biomarkers. We aim to study the diagnostic and prognostic values of pNfL in the different confidence levels of FTD diagnosis.Method: Patients fulfilling criteria for FTD were classified as “Possible”, “Probable” or “Definite” FTD based on the current diagnostic criteria. pNfL levels were determined with SiMoA, and then compared to Control and AD groups, as well as within the different levels of confidence in the FTD diagnosis.Result: 187 subjects were included: 37 controls, 79 AD and 71 FTD (11 Possible, 49 Probable and 11 Definite). The FTD group showed higher concentrations of pNfL than the Control (p < 0.001, AUC: 0.817) and the AD groups (p < 0.001, AUC: 0.677) (Figure 1A and 1B).Within the FTD group, pNfL levels differed across the different diagnostic confidence levels (Figure 1C). Subjects with Probable and Definite FTD showed higher levels of pNfL than subjects with Possible FTD (p < 0.001 and p < 0.01 respectively). By contrast, the Possible FTD group did not show differences in pNfL concentrations with the control group. In that sense, the diagnostic value of pNfL varied according to the different grades of diagnostic confidence (Figure 1D), with cases with probable or definite FTD exhibiting an excellent AUC to distinguish from controls (AUC 0.860 and 0.908 respectively), but with no discrimination between possible FTD and controls (AUC 0.583).pNfL presented an excellent performance in the differentiation of structural or functional impairment in the neuroimage (AUC 0.94, and AUC 0.86 respectively) (Figure 2). Finally, higher baseline pNfL levels were associated with worse clinical progression in FTD cases (Figure 3).Conclusion: pNfL were higher in FTD patients compared to controls or AD patients. However, we found significant variation across the different confidence levels of FTD diagnosis. Finally, pNfL predicted neuroimage alterations and a worse clinical progression in FTD patients.

Background: One of the clinical problems for biomarkers' clinical use is the ability to differentiate between Alzheimer’s disease (AD), frontotemporal dementia (FTD), and healthy subjects (CTR). This clearly challenges diagnosis and prognosis. We implemented a ML algorithm that provides individual probabilistic diagnoses for these dementias based on magnetic resonance imaging (MRI) and we correlated the results with biochemical markers.Method: We studied 3T‐T1w MRI of 432 subjects. A subset of these participants had cerebrospinal fluid (CSF) and plasma biomarkers (Table 1). We obtained regional subcortical gray matter volumes and cortical thickness measures using Freesurfer. We implemented a calibrated classifier with a Support Vector Machine with only the MRI data. We tested paired‐wise classification and classification across the 3 groups. We obtained individual probabilities associated with group correspondence. We studied the correlation between these probabilities and CSF and plasma biomarkers. For this, we subdivided the groups into true‐group (subjects with classification according to clinical diagnosis) and false‐group (subjects which did not coincide with clinical diagnosis). Finally, we implemented a permutation test to find the importance of each region in the classification.Result: We obtained accuracies of 90.7 ± 6.7% in the CTR vs AD classification, 88.6 ± 4.5% for CTR vs FTD, 79.3 ± 8.8% for AD vs FTD, and 79.9 ± 5.1% when discriminating the 3 groups. We obtained a significant positive correlation for plasma p‐tau181 for the false‐AD in the comparison AD vs CTR (Figure 1). The correlation of the false‐CTR was significantly positive for the CSF and plasma NfL. Finally, in the AD vs FTD, the true‐FTD had a significant negative correlation with CSF NfL. The other biochemical biomarkers did not provide additional information. The most important regions for classification are shown in Figure 2.Conclusion: The ML algorithm gave high accuracies. Within wrongly classified AD patients, probabilities correlated positively with plasma p‐tau181, suggesting hidden pathological processes associated with subjects clinically classified as CTR. Finally, the group probability within well‐classified FTD patients in comparison with AD negatively correlated with CSF NfL. We suggest that this approach can be used as a tool to try to develop personalized diagnoses.

Background: Neuropsychiatric symptoms, including sleep‐wake disturbances, are more common in Early‐onset Alzheimer’s disease (EOAD) than in Late‐onset AD (LOAD)(Falgàs, EurJNeurol2022). The pattern of tau‐related degeneration of wake and sleep‐promoting neurons determine sleep‐wake profiles in neurodegenerative disorders (Oh, JAMANeurol2022). We hypothesize that distinct patterns of noradrenergic (locus coeruleus‐LC) and orexigenic (hypothalamus‐HT) degeneration contribute for the differential sleep‐wake patterns between EOAD and LOAD.Method: A group of 21 participants (7 EOAD, 18 LOAD) with a diagnosis of AD confirmed by cerebrospinal fluid biomarkers (CSF) were recruited from Hospital Clínic de Barcelona. Participants underwent sleep questionnaires (Pittsburg Sleep Quality Inventory‐PSQI and Epworth Sleepiness Scale‐ESS), 2‐week actigraphy (MotionWatch8, CamnTech) and 3T‐neuromelanin‐sensitive MRI to measure locus coeruleus (LC) and hypothalamus (HT) volumes. Sleep, napping, and circadian parameters were obtained (MotionWare).Result: The preliminary results showed no differences in sex (71vs.61% women) or disease stage (CDR 0.5: 85% vs.72%). The amnestic phenotype was prevalent (100 vs.78%). EOAD had longer daytime napping (93.2±74 vs. 58.2±28min, pppp3 vs. 33.3±6 pp = 0.09), being inversely correlated to the age at onset (r = ‐0.47, p<0.05). The central phase measure was inversely correlated to LC‐volumes (r = ‐0.47, p<0.05) but not to HT. Further analyses with increased sample size, CSF noradrenaline and orexin will be performed.Conclusion: Greater daytime sleepiness and circadian dysfunction could be main differing traits regarding sleep‐wake patterns between EOAD and LOAD. An opposite pattern of degeneration within wake‐promoting systems consisting of higher degeneration of the LC‐noradrenergic system and relative preservation of the HT‐orexigenic in EOAD in contrast to LOAD might underlie differing sleep‐wake phenotypes. Age‐related hypothalamic volume loss can be adding to AD driven loss resulting in higher volumes in EOAD.

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.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T‐T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2‐year follow‐up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross‐sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross‐sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross‐sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross‐sectional and longitudinal data.

Background and purpose: How the APOE genotype can differentially affect cortical and subcortical memory structures in biomarker‐confirmed early‐onset (EOAD) and late‐onset (LOAD) Alzheimer's disease (AD) was assessed.Method: Eighty‐seven cerebrospinal fluid (CSF) biomarker‐confirmed AD patients were classified according to their APOE genotype and age at onset. 28 were EOAD APOE4 carriers (+EOAD), 21 EOAD APOE4 non‐carriers (–EOAD), 23 LOAD APOE4 carriers (+LOAD) and 15 LOAD APOE4 non‐carriers (–LOAD). Grey matter (GM) volume differences were analyzed using voxel‐based morphometry in Papez circuit regions. Multiple regression analyses were performed to determine the relation between GM volume loss and cognition.Results: Significantly more mammillary body atrophy in +EOAD compared to –EOAD is reported. The medial temporal and posterior cingulate cortex showed less GM in +LOAD compared to –LOAD. Medial temporal GM volume loss was also found in +EOAD compared to –LOAD. With an exception for +EOAD, medial temporal GM was strongly associated with episodic memory in the three groups, whilst posterior cingulate cortex GM volume was more related with visuospatial abilities. Visuospatial abilities and episodic memory were also associated with the anterior thalamic nucleus in –LOAD.Conclusions: Our results show that the APOE genotype has a significant effect on GM integrity as a function of age of disease onset. Specifically, whilst LOAD APOE4 genotype is mostly associated with increased medial temporal and parietal atrophy compared to –LOAD, for EOAD APOE4 might have a more specific effect on subcortical (mammillary body) structures. The findings suggest that APOE genotype needs to be taken into account when classifying patients by age at onset.

Background and purpose: Sex is believed to drive heterogeneity in Alzheimer's disease (AD), although evidence in early‐onset AD (EOAD; <65 years) is scarce.Methods: We included 62 EOAD patients and 44 healthy controls (HCs) with core AD cerebrospinal fluid (CSF) biomarkers, neurofilament light chain levels, neuropsychological assessment, and 3‐T magnetic resonance imaging. We measured cortical thickness (CTh) and hippocampal subfield volumes (HpS) using FreeSurfer. Adjusted linear models were used to analyze sex‐differences and the relationship between atrophy and cognition.Results: Compared to same‐sex HCs, female EOAD subjects showed greater cognitive impairment and broader atrophy burden than male EOAD subjects. In a direct female‐EOAD versus male‐EOAD comparison, there were slight differences in temporal CTh, with no differences in cognition or HpS. CSF tau levels were higher in female EOAD than in male EOAD subjects. Greater atrophy was associated with worse cognition in female EOAD subjects.Conclusions: At diagnosis, there are sex differences in the pattern of cognitive impairment, atrophy burden, and CSF tau in EOAD, suggesting there is an influence of sex on pathology spreading and susceptibility to the disease in EOAD.