Wannan, C. M. J. | Search bioRxiv

bioRxiv2023

Plasma neurofilament light protein provides evidence of accelerated brain ageing in treatment-resistant schizophrenia

BackgroundAccelerated brain aging has been observed across multiple psychiatric disorders. Blood markers of neuronal injury such as Neurofilament Light (NfL) protein may therefore represent biomarkers of accelerated brain aging in these disorders. The current study aimed to examine whether relationships between age and plasma NfL were increased in individuals with primary psychiatric disorders compared to healthy individuals. MethodsPlasma NfL was analysed in major depressive disorder (MDD, n = 42), bipolar affective disorder (BPAD, n = 121), treatment-resistant schizophrenia (TRS, n = 82), a large reference normative healthy control (HC) group (n= 1,926) and a locally-acquired HC sample (n = 59). A general linear model (GLM) was used to examine diagnosis by age interactions on NfL z-scores using the large normative HC sample as a reference group. Significant results were then validated using the locally-acquired HC sample. Resultsa GLM identified a significant age by diagnosis interaction for TRS vs HCs and BPAD vs HCs. Post hoc analyses revealed a positive correlation between NfL levels and age among individuals with TRS, whereas a negative correlation was found among individuals with BPAD. However, only the TRS findings were replicated using the locally-acquired HC sample. Post hoc analyses revealed that individuals with TRS aged <40 had lower NfL levels compared to same-age HCs, whereas individuals with TRS aged >40 had higher NfL levels compared to same-age HCs. ConclusionsThese findings add to the growing literature supporting the notion of accelerated brain ageing in schizophrenia-spectrum disorders.

neuroscience↗

bioRxiv2021

Affinity Scores: An Individual-centric Fingerprinting Framework for Neuropsychiatric Disorders

BackgroundPopulation-centric frameworks of biomarker identification for psychiatric disorders focus primarily on comparing averages between groups and assume that diagnostic groups are (1) mutually-exclusive, and (2) homogeneous. There is a paucity of individual-centric approaches capable of identifying individual-specific fingerprints across multiple domains. To address this, we propose a novel framework, combining a range of biopsychosocial markers, including brain structure, cognition, and clinical markers, into higher-level fingerprints, capable of capturing intra-illness heterogeneity and inter-illness overlap. MethodsA multivariate framework was implemented to identify individualised patterns of brain structure, cognition and clinical markers based on affinity to other participants in the database. First, individual-level affinity scores defined each participants "neighbourhood" across each measure based on variable-specific hop sizes. Next, diagnostic verification and classification algorithms were implemented based on multivariate affinity score profiles. To perform affinity-based classification, data were divided into training and test samples, and 5-fold nested cross-validation was performed on the training data. Affinity-based classification was compared to weighted K-nearest neighbours (KNN) classification. K-means clustering was used to create clusters based on multivariate affinity score profiles. The framework was applied to the Australian Schizophrenia Research Bank (ASRB) dataset. ResultsIndividualised affinity scores provided a fingerprint of brain structure, cognition, and clinical markers, which described the affinity of an individual to the representative groups in the dataset Diagnostic verification capability was moderate to high depending on the choice of multivariate affinity metric. Affinity score-based classification achieved a high degree of accuracy in the training, nested cross-validation and prediction steps, and outperformed KNN classification in the training and test datasets. ConclusionAffinity scores demonstrate utility in two keys ways: (1) Early and accurate diagnosis of neuropsychiatric disorders, whereby an individual can be grouped within a diagnostic category/ies that best matches their fingerprint, and (2) identification of biopsychosocial factors that most strongly characterise individuals/disorders, and which may be most amenable to intervention.

neuroscience↗