Pereira, Joana B.

Abstract Neuromodulatory subcortical systems (NSSs) are uniquely susceptible to dementia‐related pathology, leading to frequent molecular and behavioral impairments associated with altered function of these nuclei. Some of these systems display clear sex‐specific cytoarchitecture and signaling leading to distinct physiology and behavioral outputs in males and females, while other regions display nominal sex differences. However, the relevance of sex differences in modulating dysfunction of NSSs in Alzheimer's disease (AD) and related dementias is not well understood. This review is a joint effort by the Neuromodulatory Subcortical Systems and Sex and Gender Differences in Alzheimer's Disease Professional Interest Areas of the Alzheimer's Association. We review sex differences in NSSs, both in non‐disease states and in AD models and patients. We highlight the possible role of NSSs in driving sex‐specific AD susceptibility and potential footholds for sex‐based interventions targeting these systems. We conclude by outlining immediate and long‐term actions to address the intersection of NSSs, sex, and AD.

Abstract Introduction As cerebrospinal fluid (CSF) neurofilament light protein (NfL) and the CSF/serum albumin ratio (Q Alb ) are used in the clinical routine, the impact of demographic factors on these biomarkers is important to understand. Methods Participants were derived from two Swedish samples: the population‐based H70 Study (n = 308, age 70) and a clinical routine cohort (CSF NfL, n = 8995, Q Alb , n = 39252, age 0 to 95). In the population‐based study, Q Alb and NfL were examined in relation to sex, cardiovascular risk factors, and cerebral white matter lesions (WMLs). In the clinical cohort, Q Alb and NfL sex differences were tested in relation to age. Results Men had higher Q Alb and NfL concentrations and had higher Q Alb and NfL concentrations from adolescence throughout life. NfL was not related to WML, but Q Alb correlated positively with WMLs. Discussion The CSF NfL sex difference could not be explained by vascular pathology. Future studies should consider using different reference limits for men and women.

AbstractBackgroundComputed tomography (CT) is the most commonly used brain examination tool for the initial assessment of neurodegenerative diseases such as dementia disorders. It is widely available, affordable and provides short scan times, however, it is mainly used for visual assessment of brain integrity and exclusion of co‐pathologies while magnetic resonance imaging (MRI) is the preferred modality for the extraction of regional brain volumetric measures, obtained by brain segmentation into tissue classes and anatomical regions. We developed an automated, deep learning‐based approach to segment CT scans into intracranial volume (ICV), grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF).Method139 CT and T1‐weighted MRI scans acquired on the same day in participants of the Gothenburg H70 Birth Cohort Study (70% female) were included in this study. SPM12 was used to extract GM, WM and CSF tissue class labels and Pincram for creating ICV masks from the MRIs. These labels were used as training input and as the reference criteria for subsequent volumetric and similarity comparisons. In a training subcohort (n=120), CT scans were co‐registered to their corresponding MRI. A U‐Net deep learning model was developed using the Keras software package which underwent two training sessions: 1. Predict ICVs in CT and extract brain; 2. Predict GM, WM and CSF volumes with extracted CT images as input, preserving the predictions in individual space. MRI labels were incorporated during the training sessions including 120 datasets.ResultNineteen validation datasets which were not included in the training were used for the prediction process without MRI intervention. Continuous Dice scores between ICV, GM, WM and CSF predictions from the model in the validation datasets and independently MRI‐derived labels were 0.97, 0.79, 0.81 and 0.72 with a volumetric Pearson correlation coefficient, r of 0.93, 0.84, 0.73 and 0.71, respectively.ConclusionOur pilot study developed a deep learning model that enables the automated segmentation and volumetric measure extraction from cranial CT. With further optimization, training on larger and more diverse datasets, we aim at improving the model to execute predictions comparable to MRI‐based approaches, thus, developing a diagnostic support tool based on routinely acquired CT scans.

AbstractBackgroundCSF neurofilament light protein (NfL) is an important biomarker in several neurologic diseases. CSF NfL concentrations are known to increase with age, but sex differences have been less examined. The CSF/serum albumin ratio (QAlb) is a biomarker for blood‐brain barrier integrity, which has previously been found to be higher in men. As NfL and QAlb are used in clinical routine, the impact of demographic factors on these biomarkers is important to understand.MethodParticipants were derived from two samples. The first was a population‐based cohort of 308 70‐year olds from the Gothenburg H70 Birth Cohort Study, Sweden. The second data set comprised CSF NfL and QAlb results from 8995 and 39252 subjects respectively, generated in clinical practice between 2005‐2012 in Gothenburg, Sweden. In the population‐based study, QAlb and NfL concentrations were examined in relation to sex, cardiovascular risk factors, and MRI signs of cerebral white matter lesions. In the clinical routine cohort, sex differences in QAlb and NfL were tested in relation to age.ResultIn the population‐based sample, men had higher QAlb and NfL concentrations than women. NfL was not related to cerebral vascular pathology, but QAlb correlated positively with several measures of white matter lesions. In the clinical routine cohort, men also had higher QAlb and NfL concentrations from adolescence and throughout life.ConclusionQAlb ratios and CSF NfL levels are higher in men throughout life. The CSF NfL sex difference could not be explained by vascular pathology. Future studies should consider using different reference limits for men and women.