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BackgroundWhite matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.MethodsWe used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).ResultsAcross tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.ConclusionTo conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.

Detection and measurement of amyloid‐beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical‐based and biological‐based classifications. ArcheD‐predicted Aβ CSF values correlated with measured Aβ CSF values (r = 0.92; q < 0.01), SUVR (rAV45 = −0.64, rFBB = −0.69; q < 0.01) and episodic memory measures (0.33 < r < 0.44; q < 0.01). For both classifications, cerebral white matter significantly contributed to CSF prediction (q < 0.01), specifically in non‐symptomatic and early stages of AD. However, in late‐stage disease, the brain stem, subcortical areas, cortical lobes, limbic lobe and basal forebrain made more significant contributions (q < 0.01). Considering cortical grey matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination. [ABSTRACT FROM AUTHOR]

The mechanistic target of rapamycin complex 1 (mTORC1) inhibitor everolimus is one of the few approved therapies for locally advanced and metastatic neuroendocrine tumours (NETs). However, after initial disease stabilisation, most patients develop resistance within 1 year. Our aim was to overcome resistance to everolimus by additional treatment with the PI3K-alpha inhibitor alpelisib in an everolimus-resistant orthotopic pancreatic neuroendocrine carcinoma xenograft mouse model. Female SCID mice underwent laparoscopic pancreatic transplantation of everolimus-sensitive (BON1KDMSO) or everolimus-resistant (BON1RR2) NET cells. Both groups were further divided into four treatment groups: placebo, everolimus, alpelisib, and everolimus + alpelisib (combination). Oral treatment was started at a tumour volume of approximately 140 mm3 and continued until 1900-2000 mm3, validated by weekly MRI. Somatostatin receptor expression and tumour viability were analysed by 68Ga-DOTATOC and 18F-FDG PET/CT. Everolimus resistance of the BON1RR2 tumours was confirmed. In the everolimus-sensitive group, everolimus alone, alpelisib alone, and combination treatment significantly prolonged survival, compared to placebo, while in the BON1RR2 group, only combination treatment significantly prolonged survival compared to placebo, but neither everolimus nor alpelisib alone. Placebo-treated everolimus-sensitive tumours grew more rapidly (median survival 45 days), compared to placebo-treated everolimus-resistant tumours (60 days). Within the everolimus-sensitive group, the combination-treated mice showed the longest median survival (52 days). Of all groups, the everolimus-resistant combination-treated group survived longest (69 days). Combination treatment with everolimus and alpelisib seems promising to overcome everolimus resistance in neuroendocrine neoplasms, and should be further examined in a clinical trial. [ABSTRACT FROM AUTHOR]

Introduction: Both elevated cortisol and hippocampal volume have been linked to an increased risk for the development of Alzheimer's disease (AD). This longitudinal study assessed the effects of plasma cortisol on hippocampal atrophy and clinical progression rates in patients with mild cognitive impairment (MCI). Methods: Patients with amnestic MCI (n = 304) were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on availability of baseline plasma cortisol and hippocampal volume measures, assessed at baseline and during follow‐ups. We investigated associations between plasma cortisol, hippocampal volume, and risk of clinical progression to AD over a study period of up to 100 months (mean follow‐up time 36.8 months) using linear mixed models, Cox proportional hazards models, and Kaplan‐Meier estimators. Results: Plasma cortisol predicted greater hippocampal atrophy, such that participants with higher cortisol showed faster decline in hippocampal volume over time (interaction: β = ‐0.15, p = 0.004). Small hippocampal volume predicted a higher risk of clinical progression to AD (haard ratio [HR] = 2.15; confidence in terval [CI], 1.64–2.80; p < 0.001). A similar effect was not found for cortisol (HR = 1.206; CI, 0.82–1.37; p = 0.670) and there was no statistical evidence for an interaction between hippocampal volume and cortisol on clinical progression (HR = 0.81; CI, 0.57–0.17; p = 0.260). Discussion: Our findings suggest that higher cortisol predicts higher hippocampal atrophy, which in turn is a risk factor for progression to AD. Regulation of the hypothalamic‐pituitary‐adrenal axis through stress‐reducing lifestyle interventions might be a protective factor against hippocampal degeneration at the prodromal stage of AD. [ABSTRACT FROM AUTHOR]

Introduction: Limbic age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC) is common in advanced age and can underlie a clinical presentation mimicking Alzheimer's disease (AD). We studied whether an autopsy‐derived fluorodeoxyglucose positron emission tomography (FDG‐PET) signature of LATE‐NC provides clinical utility for differential diagnosis of amnestic dementia patients. Methods: Ante mortem FDG‐PET patterns from autopsy‐confirmed LATE‐NC (N = 7) and AD (N = 23) patients were used to stratify an independent cohort of clinically diagnosed AD dementia patients (N = 242) based on individual FDG‐PET profiles. Results: Autopsy‐confirmed LATE‐NC and AD groups showed markedly distinct temporo‐limbic and temporo‐parietal FDG‐PET patterns, respectively. Clinically diagnosed AD dementia patients showing a LATE‐NC–like FDG‐PET pattern (N = 25, 10%) were significantly older, showed less abnormal AD biomarker levels, lower APOE ε4, and higher TMEM106B risk allele load. Clinically, they exhibited a more memory‐predominant profile and a generally slower disease course. Discussion: An autopsy‐derived temporo‐limbic FDG‐PET signature identifies older amnestic patients whose clinical, genetic, and molecular biomarker features are consistent with underlying LATE‐NC. [ABSTRACT FROM AUTHOR]

Background: WMH in the corpus callosum (CC), as previously related to AD pathology(1) and lower cognition in AD patients(2), are suggested to play an important role in AD. The Aim of this study was to substantiate the associations between WMH location, biomarkers of AD pathophysiology, and cognitive performance in the DELCODE study. Method: 429 participants were included (HC: 126, SCD: 165, MCI: 87, AD: 51). WMH volumes were segmented from FLAIR images. WMH volumes were adjusted for total intracranial volume and log‐transformed. Beta‐Amyloid and total Tau (Aß42 and tTau, measured in CSF) and neurodegeneration (ND, measured by hippocampal volume) were log‐transformed and used as surrogate markers for AD pathophysiology. Cognitive performance was assessed by a global cognitive composite. Path modelling, adjusted for age, sex and education, was performed to estimate direct and indirect associations between regional WMH volumes (in the CC splenium, body, genu and the frontal lobe as possible control region), AD biomarkers and cognition. Results: The correlation matrix (Figure 1) showed the highest correlation of Aß42 and tTau with WMH volume the splenium CC. Greater WMH volume in the splenium CC was directly associated with lower global cognition (Figure 2a, ß = ‐0.10, p = 0.01) and indirectly through Aß42 (ß = ‐0.10, p = 0.001) and ND (Figure 2b, ß = ‐0.05, p = 0.001). The same indirect association was statistically significant regarding tTau (ß = ‐0.03, p < 0.001). Model outcomes were similar for the body and genu of the CC. For frontal WMH volume, the direct association with global cognition was not statistically significant (ß = ‐0.002, p = 0.96), only through indirect associations with Aß42 (ß = ‐0.05, p = 0.003) and ND (ß = ‐0.02, p = 0.05). Conclusion: WMH in the CC are associated with lower cognitive performance through direct and indirect associations with Aß42 and neurodegeneration. Similar associations were seen for frontal WMH. However, a direct association between frontal WMH volume and cognition was not noticed, when considering AD biomarkers. Our study substantiates the clinical relevance and impact of WMH in the CC, in particular the splenium, in AD. 1)Gaubert et al.,2021 2)Garnier‐Crussard et al.,2021 [ABSTRACT FROM AUTHOR]