Skoog, Ingmar

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.

Abstract Introduction Three cerebrospinal fluid (CSF) markers of neurodegeneration (N) (neurofilament light [NfL], total‐tau [T‐tau], and neurogranin [Ng]) have been proposed under the AT(N) scheme of the National Institute on Aging–Alzheimer's Association Research Framework. Methods We examined, in a community‐based population (N = 777, aged 50–95) (1) what variables were associated with each of the CSF (N) markers, and (2) whether the variables associated with each marker differed by increased brain amyloid. CSF T‐tau was measured with an automated electrochemiluminescence Elecsys immunoassay; NfL and Ng were measured with in‐house enzyme‐linked immunosorbent assays. Results Multiple variables were differentially associated with CSF NfL and T‐tau levels, but not Ng. Most associations were attenuated after adjustment for age and sex. T‐tau had the strongest association with cognition in the presence of amyloidosis, followed by Ng. Variables associations with NfL did not differ by amyloid status. Discussion Understanding factors that influence CSF (N) markers will assist in the interpretation and utility of these markers in clinical practice.

Abstract Increasing evidence recognizes Alzheimer's disease (AD) as a multifactorial and heterogeneous disease with multiple contributors to its pathophysiology, including vascular dysfunction. The recently updated AD Research Framework put forth by the National Institute on Aging–Alzheimer's Association describes a biomarker‐based pathologic definition of AD focused on amyloid, tau, and neuronal injury. In response to this article, here we first discussed evidence that vascular dysfunction is an important early event in AD pathophysiology. Next, we examined various imaging sequences that could be easily implemented to evaluate different types of vascular dysfunction associated with, and/or contributing to, AD pathophysiology, including changes in blood‐brain barrier integrity and cerebral blood flow. Vascular imaging biomarkers of small vessel disease of the brain, which is responsible for >50% of dementia worldwide, including AD, are already established, well characterized, and easy to recognize. We suggest that these vascular biomarkers should be incorporated into the AD Research Framework to gain a better understanding of AD pathophysiology and aid in treatment efforts.