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Background: Cross-sectional studies have identified that the prevalence of neuropsychiatric symptoms (NPS) in Parkinson&rsquo, s disease (PD) ranges from 70&ndash, 89%. However, there are few longitudinal studies determining the impact of NPS on quality of life (QoL) in PD patients and their caregivers. We seek to determine the progression of NPS in early PD. Methods: Newly diagnosed idiopathic PD cases (n = 212) and age-matched controls (n = 99) were recruited into a longitudinal study. NPS were assessed using the Neuropsychiatric Inventory with Caregiver Distress scale (NPI-D). Further neuropsychological and clinical assessments were completed by participants, with reassessment at 18 and 36 months. Linear mixed-effects modelling determined factors associated with NPI-D and QoL over 36 months. Results: Depression, anxiety, apathy and hallucinations were more frequent in PD than controls at all time points (p &lt, 0.05). Higher motor severity at baseline was associated with worsening NPI-D scores over time (&beta, = 0.1, p &lt, 0.05), but not cognition. A higher NPI total score was associated with poorer QoL at any time point (&beta, = 0.3, p &lt, 0.001), but not changed in QoL scores. Conclusion: NPS are significantly associated with poorer QoL, even in early PD. Screening for NPS from diagnosis may allow efficient delivery of better support and treatment to patients and their families.

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia—we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.