Cohort studies and randomised clinical trials (RCTs) can provide promising and valuable evidence for early identifications of at-risk individuals, informing and guiding early-stage interventions and offering evidence-based health services. However, the individual differences, or heterogeneity, embedded within various neurophenotypes are considered the main source of variability across controversial and frequently contradictory findings.

Particularly, the predictive modelling and evaluation of brain systems has become challenging due to multiple confounding factors, such as chronological and biological age, co-morbid neurological and psychiatric disorders, other brain states, medication and dosage, or intervention paradigms, among others. There is therefore a need for research to enhance our understanding on how to quantify, model, analyse, and measure the individual differences in cohort studies and clinical trials. Understanding to what extent these differences can be decoded into biomarkers or phenotypes can also help to interpret the personalised trajectory of brain health.

This Special Issue seeks to explore how disease-specific neurophenotypes can be modelled from different levels and dimensions, such as age, inter-individual, intra-individual, among others, by using quantitative measures such as network complexity, agent-based and Monte Carlo simulations, structural equation modelling, Bayesian networks, data-driven analytics, and reliability theory research.

Potential topics include but are not limited to the following:

• Novel markers to predict treatment response in neurological and psychiatric disorders
• Emerging analytic strategies for disease-specific neurophenotypes
• The impact of modifiable factors, such as vascular risks, in disease progression
• Achieving cognitive resilience in complex or interdependent systems
• Behavioural modelling for diagnosis and prognosis of brain disorders
• Impact of complexity and uncertainty on treatment-induced neural plasticity
• Vulnerability and recovery modelling of systems against ageing and neuroprogression