This study investigated the ability of magnetic resonance spectroscopy ( 1 H-MRS) of posterior cingulate cortex (PCC) and brain volumetry to predict the progression from mild cognitive impairment (MCI) to Alzheimer's Disease (AD) on the basis of clinical classification at 2 years follow-up. Thirty-eight MCI patients, eighteen healthy older adults and twenty-three AD patients were included in this study. All participants underwent a brain-MR protocol (1.5 T GE scanner) including high-resolution T1-weighted volumetric sequence (isotropic 1mm 3 ). Voxel-wise differences in brain volumetry were evaluated using FreeSurfer software and all volumes were normalized by the total intracranial volume (TIV). Careful localization of 1 H-MRS volume of PCC was performed and data were processed with the LCModel program. MCI patients underwent a complete neuropsychological assessment at baseline and were clinically re-evaluated after a mean of 28 months; twenty-six MCI patients (68.4%) converted to AD and twelve remained stable. At baseline these two MCI subgroups did not differ in the global cognitive level (Mini Mental State Examination, MMSE) or in any of the other cognitive domains; the NAA/ mI ratio in the PCC was able to differentiate MCI converters from those MCI that did not develop AD (p = 0.022) with a level of accuracy (AUC area) of 0.779. A significantly reduced volume of parahippocampal gyrus (p = 0.010) and fusiform gyrus (p = 0.026) were found in the converter MCI subgroup compared to the stable MCI subgroup. The combined use of both N- acetyl-aspartate (NAA)/myo-Inositol (mI) ratio and volume of parahippocampal gyrus, increases the overall accuracy (AUC = 0.910) in predicting the conversion to AD two years before the development of clinical symptoms. Additional longitudinal studies with a broader representative sample of MCI patients and longer follow-up might be helpful to confirm these results and to elucidate the role of each parameter in predicting the possible progression to AD, and also to all the other non-AD dementia subtypes.
Autosomal-dominant leukodystrophy (ADLD) is a rare fatal neurodegenerative disorder with overexpression of the nuclear lamina component, Lamin B1 due to LMNB1 gene duplication or deletions upstream of the gene. The molecular mechanisms responsible for driving the onset and development of this pathology are not clear yet. Vacuolar demyelination seems to be one of the most significant histopathological observations of ADLD. Considering the role of oligodendrocytes, astrocytes, and leukemia inhibitory factor (LIF)-activated signaling pathways in the myelination processes, this work aims to analyze the specific alterations in different cell populations from patients with LMNB1 duplications and engineered cellular models overexpressing Lamin B1 protein. Our results point out, for the first time, that astrocytes may be pivotal in the evolution of the disease. Indeed, cells from ADLD patients and astrocytes overexpressing LMNB1 show severe ultrastructural nuclear alterations, not present in oligodendrocytes overexpressing LMNB1. Moreover, the accumulation of Lamin B1 in astrocytes induces a reduction in LIF and in LIF-Receptor (LIF-R) levels with a consequential decrease in LIF secretion. Therefore, in both our cellular models, Jak/Stat3 and PI3K/Akt axes, downstream of LIF/LIF-R, are downregulated. Significantly, the administration of exogenous LIF can partially reverse the toxic effects induced by Lamin B1 accumulation with differences between astrocytes and oligodendrocytes, highlighting that LMNB1 overexpression drastically affects astrocytic function reducing their fundamental support to oligodendrocytes in the myelination process. In addition, inflammation has also been investigated, showing an increased activation in ADLD patients’ cells.
Stroke significantly impacts quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need for a better prediction of long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnections-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace to predict neuropsychological scores one year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights available as the first comprehensive atlas of disconnectome-deficit relations across 86 neuropsychological scores. Out-of-sample prediction derived from this atlas achieved average accuracy over 80%, which is higher than any other framework. Our novel predictive framework is available as an interactive web application, the disconnectome symptoms discoverer (http://disconnectomestudio.bcblab.com), to provide the foundations for a new and practical approach to modelling cognition in stroke. Our atlas and web application will reduce the burden of cognitive deficits on patients, their families, and wider society while also helping to tailor personalized treatment programs and discover new targets for treatments. We expect the range of assessments and the predictive power of our framework to increase even further through future crowdsourcing.
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