Brain Biomarker Solutions

Brain Biomarker Solutions

Description

Developing antibodies and analytical methods for diagnosing and monitoring treatments for brain diseases.

Brain Biomarker Solutions

Address

Erik Dahlbergsgatan 11 A, Gothenburg 41126
Gothenburg, SWEDEN

Contact

jesper.dahlberg@ventures.gu.se

Description

Developing antibodies and analytical methods for diagnosing and monitoring treatments for brain diseases.

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Position on the value chain

With the ultimate aim to create a European alliance for bioproduction in Europe, organisations have joined forces with COBIOE. Discover who are our associates.

Bio-resources and biobanking
  • Cells, tissues and humanized xeno-organs
  • Biosamples
  • Viral, phage or bacterial specimen
Identification of biotherapies
  • Target identification
  • Target validation
  • Screening
Drug design
  • Drug assessment
  • Drug engineering
  • In vitro preclinical studies
Clinical validation
  • Clinical trials
  • In vivo preclinical validation
  • Pre-industrial scale production
Production
  • Upstream processes
  • Downstream processes
  • Quality control
Market access
  • CE mark / market authorisation
  • Payment / Reimbursement
  • Care pathways

Brain Biomarker Solutions last news

07/03/2024

Subtypes of brain change in aging and their associations with cognition and Alzheimer's disease biomarkers

Structural brain changes underly cognitive changes in older age and contribute to inter-individual variability in cognition. Here, we assessed how changes in cortical thickness, surface area, and subcortical volume, are related to cognitive change in cognitively unimpaired older adults using structural magnetic resonance imaging (MRI) data-driven clustering. Specifically, we tested (1) which brain structural changes over time predict cognitive change in older age (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers phosphorylated tau (p-tau) and amyloid-{beta}(A{beta}42), and (3) the degree of overlap between clusters derived from different structural features. In total 1899 cognitively healthy older adults (50 - 93 years) were followed up to 16 years with neuropsychological and structural MRI assessments, a subsample of which (n = 612) had CSF p-tau and A{beta}42 measurements. We applied Monte-Carlo Reference-based Consensus clustering to identify subgroups of older adults based on structural brain change patterns over time. Four clusters for each brain feature were identified, representing the degree of longitudinal brain decline. Each brain feature provided a unique contribution to brain aging as clusters were largely independent across modalities. Cognitive change and baseline cognition were best predicted by cortical area change, whereas higher levels of p-tau and A{beta}42 were associated with changes in subcortical volume. These results provide insights into the link between changes in brain morphology and cognition, which may translate to a better understanding of different aging trajectories.