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Background: The effect of amyloid‐b brain deposition on cognition is still debated, since it has been shown that its accumulation begins almost 15 years before cognitive symptoms' onset, then reaches a plateau while cognition continues to decline. We studied if there is a parallel between amyloid‐b deposition and cognitive performances in young‐onset Mild Cognitive Impairment (MCI) patients, and if it is associated to symptoms' duration. Method: Subjects with a diagnosis of MCI and symptoms' onset ≤ 65 years underwent neuropsychological assessment, Magnetic Resonance Imaging, and (18F)Flutemetamol‐PET (amy‐PET). FMRIB Software Library was used to assess voxel‐wise correlations between amyloid‐b deposition and cognitive performances, adjusted for age, sex, education, and degree of grey matter atrophy. Correlations were repeated grouping patients depending on whether the duration of their cognitive symptoms had been shorter or longer than the median of the entire group (34 months). Result: Among the 30 recruited individuals we found a statistically significant correlation between amyloid‐b burden and performance in attentional‐executive (i.e., Trail Making Test [TMT]‐A/B and Stroop Test) and memory (i.e., Free and Cued Selective Reminding Test [FCSRT]‐immediate and delayed free recall, Corsi Supraspan, and Rey‐Osterrieth Complex Figure‐delayed recall) tests. Patients with short disease duration (n = 15) had even more significant associations relative to patients with longer duration. More precisely, they showed significant linear correlations in widespread brain regions between amyloid‐b burden and cognition in attentional‐executive (i.e., Attentional Matrices, Raven's Coloured Progressive Matrices, TMT‐A/B) and memory (i.e., Babcock Short Tale‐immediate/delayed recall, FCSRT‐immediate free recall, and Corsi Supraspan) tests. Conclusion: In young‐onset MCI we found a linear relationship between amyloid‐b deposition and cognitive performance, which is more evident in patients with shorter disease duration. Our results contribute to identifying which cognitive tests are most sensitive to amyloid‐b deposition in the initial stages of dementing diseases. [ABSTRACT FROM AUTHOR]

Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus, which are among the earliest and most frequent brain changes associated with Alzheimer disease (AD). However, CNNs typically achieve the highest accuracy on datasets acquired from the same domain as the training dataset. Transfer learning allows domain adaptation through further training on a limited dataset. In this study, we applied transfer learning on a network called spatial warping network segmentation (SWANS), developed and trained in a previous study. We used MR images of patients with clinical diagnoses of mild cognitive impairment (MCI) and AD, segmented by two different raters. By using transfer learning techniques, we developed four new models, using different training methods. Testing was performed using 26% of the original dataset, which was excluded from training as a hold‐out test set. In addition, 10% of the overall training dataset was used as a hold‐out validation set. Results showed that all the new models achieved better hippocampal segmentation quality than the baseline SWANS model (ps <.001), with high similarity to the manual segmentations (mean dice [best model] = 0.878 ± 0.003). The best model was chosen based on visual assessment and volume percentage error (VPE). The increased precision in estimating hippocampal volumes allows the detection of small hippocampal abnormalities already present in the MCI phase (SD = [3.9 ± 0.6]%), which may be crucial for early diagnosis. [ABSTRACT FROM AUTHOR]

Background: The term "Cognitive Reserve" (CR) refers to the ability of the brain to cope with the damage in order to minimize symptomatology. Several studies have shown controversial data about functional and structural correlates of CR in normal and pathological aging, probably due to the use of different CR proxies (level of education, occupational complexity, participation in leisure activities, IQ, verbal abilities or intelligence, heterogeneous fMRI tasks). The aim of this study was to investigate imaging correlates of different aspects of CR in a sample of patients with Mild Cognitive Impairment (MCI). Method: Patients with a diagnosis of MCI and young onset of cognitive symptoms (< 65) answered the Cognitive Reserve Index questionnaire (CRIq) aimed at measuring cognitive reserve acquired during lifetime. Patients also underwent multimodal Magnetic Resonance Imaging (MRI) on a 3T scanner including high‐resolution T1 and functional MRI sequences acquired at rest (resting fMRI). CRIq‐Total score and single subscores (CRIq‐Education, ‐WorkingActivity and ‐LeisureTime) were related to regional grey matter (GM) volume, whole brain fractional anisotropy (FA) and functional connectivity of resting‐state networks (RSNs) using, respectively, Voxel Based Morphometry (VBM), Tract‐Based Spatial Statistics (TBSS), and independent component analysis of resting fMRI (MELODIC). Result: Thirty‐nine patients were recruited. The median CRIq‐Total, ‐Education, ‐WorkingActivity and ‐LeisureTime scores were respectively 102, 97, 105 and 105. VBM correlational analysis showed no significant results. TBSS revealed a negative correlation between CRIq‐LeisureTime and FA of the body of corpus callosum. Resting‐fMRI analyses revealed negative correlations between CRIq scores and functional connectivity, involving particularly frontal and temporal areas. Conclusion: In patients with MCI, cognitive reserve relates to functional connectivity of fronto‐temporal areas rather than to structural variability (grey matter atrophy or white matter microstructural integrity). The negative correlations found between functional connectivity and CRIq scores shows that patients with high cognitive reserve may not need to increase functional connectivity. [ABSTRACT FROM AUTHOR]

Background: Mild Cognitive Impairment (MCI) is a clinical condition that captures the stage between normal aging and dementia along the continuum of cognitive impairment. While it is usually thought to be precursor of Alzheimer's Disease, when occurring at young ages (i.e., before 65 years old) it may increase the risk of all forms of neurodegenerative dementing diseases. We studied which neuropsychological and biomarker characteristics are predictors of conversion to dementia in a sample of patients with young‐onset MCI. Method: Subjects with a diagnosis of MCI and early‐onset of cognitive symptoms (< 65 yo) were recruited in a prospective longitudinal study. They underwent extended neuropsychological assessment, Magnetic Resonance Imaging with measurement of hippocampal volume, and lumbar puncture with measurements of cerebrospinal fluid (CSF) light‐chain neurofilaments (NFL), beta‐amyloid 1‐42, total‐ and phosphorylated‐tau. They were followed‐up clinically and repeated the neuropsychological evaluation at 18 months from the baseline. Statistical analyses were performed with Stata15. Each biomarker and neuropsychological variable were added in multivariate Cox regression models with age, sex, and education. Result: 84 young‐onset MCI patients were recruited. After 18 months, 26 converted to dementia while 58 remained stable (i.e., MCI). Preliminary analyses showed that baseline smaller bilateral hippocampal volumes, lower baseline MMSE scores, baseline worst performances in tests of language, verbal long‐term memory, and executive functions, as well as baseline elevated levels of CSF NFLs increased the risk of conversion to dementia. Among them, the higher risk was given by the Free and Cued Selective Reminding Test (FCSRT), the Corsi Supraspan, the Frontal Assessment Battery, and the CSF NFLs levels, as shown by spline regression analyses (Figure 1). Of note, the FCSRT delayed recall showed a U‐shaped relationship with the risk of conversion (i.e., not only low/bad performance but also high/good performance were both related to a higher risk of conversion) suggesting that the types of predicted conversion may be different. Conclusion: Our results suggest that, in young‐onset MCI, a combination of CSF‐NFLs levels and baseline performance at long‐term memory tests may be predictive of conversion to dementia, either AD or FTD. [ABSTRACT FROM AUTHOR]