Nearly 25% of people with intellectual disability (PwID) have epilepsy compared to 1% of the UK general population. PwID are commonly excluded from research, eventually affecting their care. Understanding seizures in PwID is particularly challenging because of reliance on subjective external observation and poor objective validation. Remote electroencephalography (EEG) monitoring could capture objective data, but particular challenges and implementation strategies for this population need to be understood.

This co-production aimed to explore the accessibility and potential impact of a remote, long-term EEG tool (UnEEG 24/7 SubQ) for PwID and epilepsy.

We conducted six, 2-hour long workshops; three with people with mild intellectual disability and three with families/carers of people with moderate–profound intellectual disability. Brief presentations, easy read information and model demonstrations were used to explain the problem and device. A semi-structured guide developed by a communication specialist and art-based techniques facilitated discussion with PwID. For family/carers, active listening was employed. All conversations were recorded and transcribed. Artificial intelligence-based coding and thematic analysis (ATLAS.ti and ChatGPT) were synthesised with manual theming to generate insights.

Co-production included four PwID, five family members and seven care professionals. Three main themes were identified: (1) perceived benefits for improving seizure understanding, informing care and reducing family and carer responsibility to accurately identify seizures; (2) the device was feasible for some PwID but not all; and (3) appropriate person-centred communication is essential for all stakeholders to reduce concerns.

The workshops identified key benefits and implementing barriers to SubQ in PwID.

Cardiovascular risk factors and white matter hyperintensities predict the progression and severity of cognitive symptoms in PD. While controversial, emerging evidence suggests that cerebrovascular dysfunction is an etiological driver of protein aggregation in neurodegenerative conditions, highlighting a need to understand how cerebrovascular function impacts cognitive function in PD. MRI cerebrovascular reactivity (CVR) paradigms provide an opportunity to measure the ability of the cerebral vessels to dilate or constrict in response to challenges. The current study evaluates whether whole brain CVR measures, degree of response (fit) and delay differ in PD with normal cognition (PD-NC) and PD with mild cognitive impairment (PD-MCI) relative to healthy controls (HC). Additionally, we evaluate if these metrics are associated with cognitive performance.

8 PD-NC, 11 PD-MCI and 11 age and sex-matched healthy controls (HC) participated in the study. PD participants were diagnosed with MCI based on the Movement Disorders Society Task force, Level II assessment (comprehensive assessment). Participants were asked to inhale gas enriched in CO2 to elicit a vasodilatory response while undergoing bold oxygen level-dependent magnetic resonance image (MRI). Whole brain fit to an end-tidal CO2 regressor and delay were used to quantify CVR in each participant. An analysis of covariance (ANCOVA) was used to evaluate group differences between HC, PD-NC, and PD-MCI in the whole brain fit and delay CVR measures accounting for age, sex, and education. Multiple regressions were conducted for each cognitive variable with whole brain fit and delay as the dependent variables adjusting for age, sex, and education.

A significant main effect of group was observed for whole brain CVR latency (F(2, 23) = 4.227; p = 0.027). Post hoc tests were not significant, though indicated a trend that PD-NC (18.14 ±1.94) and PD-MCI (18.15 ± 1.55) patients exhibited longer delays relative to HC (15.84 ± 2.37). Regression results indicated limited relationships between CVR measures and cognitive functioning.

PD patients (PD-NC and PD-MCI) exhibited longer CVR delays relative to HC, suggesting a delayed vasodilatory response in PD. Examination of the association between CVR metrics and cognition were not significant, though these results should be interpreted with caution given the small sample size.

Neuromelanin imaging is an emerging biomarker for PD as it captures degeneration of the midbrain, a process which is associated with the motor symptoms of the disease. Currently, it is unknown whether this degeneration also contributes to cognitive dysfunction in PD beyond dysfunction associated with fronto-subcortical systems, as quantitative examination of substantia nigra (SN) degeneration could not be studied until recently.

In the current study, we examine whether neuromelanin signal is associated with broader cognitive dysfunction in PD patients with varying degrees of cognitive impairment: PD with normal cognition (PD-NC), PD with mild cognitive impairment (PD-MCI), and healthy controls (HC).

11 PD-NC, 16 PD-MCI and 14 age and sex-matched healthy controls (HC) participated in the study. PD participants were diagnosed with MCI based on the Movement Disorders Society Task force, Level II assessment (comprehensive assessment). In addition, all participants underwent an MRI scan that included a T1-weighted sequence and a neuromelanin-sensitive (NM-MRI) sequence. Contrast-to-noise-ratio of the substantia nigra pars compacta (SNc) was calculated and a distribution-corrected z-score was used to identify the number of extrema voxels for each individual, suggestive of the number of voxels that have exhibited significant degeneration (extrema_count). An analysis of covariance (ANCOVA) was used to evaluate group differences between HC, PD-NC, and PD-MCI in the extrema_count accounting for age, sex, and education. A multiple regression for each cognitive variable with extrema_count as the dependent variable adjusting for age, sex, and education were conducted.

A significant main effect of group (F(2, 33) = 33.548 ; p < 0.001) indicated that PD-NC (21.55 ± 12.57) and PD-MCI (43.64 ± 32.84) patients exhibited significantly greater extrema_counts relative to HC (3.36 ± 3.61; both p < 0.001). Regression results indicated that higher extrema_counts were associated with worse cognitive performance across cognitive domains, including working memory (Digit Span Backward; R2 = .357, F(1,20) = 5.295, p = .032), (Hopkins Verbal Learning Test - Revised, Trials 1 to 3; R2 = .432, F(1,20) = 5.819, p = .026).

PD patients (PD-NC and PD-MCI) exhibited decreased neuromelanin in the SNc relative to healthy controls, confirming the ability of the NM-MRI sequence to differentiate PD from HC. There was no significant difference in SNc neuromelanin levels between PD-NC patients and PD-MCI patients, however, this is likely due to the small sample size. In addition, significant SNc degeneration was associated with worse cognitive performances in tasks associated with working memory and executive functioning. These results warrant further examination of the role of SN in PD patients with differing levels of cognitive impairment.

PD patients commonly exhibit executive dysfunction early in the disease course which may or may not predict further cognitive decline over time. Early emergence of visuospatial and memory impairments, in contrast, are more consistent predictors of an evolving dementia syndrome. Most prior studies using fMRI have focused on mechanisms of executive dysfunction and have demonstrated that PD patients exhibit hyperactivation that is dependent on the degree of cognitive impairment, suggestive of compensatory strategies. No study has evaluated whether PD patients with normal cognition (PD-NC) and PD patients with Mild Cognitive Impairment (PD-MCI) exhibit compensatory activation patterns during visuospatial task performance.

10 PD-NC, 12 PD-MCI, and 14 age and sex-matched healthy controls (HC) participated in the study. PD participants were diagnosed with MCI based on the Movement Disorders Society Task Force, Level II assessment (comprehensive assessment). Functional magnetic resonance imaging (fMRI) was performed during a motion discrimination task that required participants to identify the direction of horizontal global coherent motion embedded within dynamic visual noise under Low and High coherence conditions. Behavioral accuracy and functional activation were evaluated using 3 * 2 analyses of covariance (ANCOVAs) (group [HC, PD-NC, PD-MCI] * Coherence [High vs. Low]) accounting for age, sex, and education. Analyses were performed in R (v4.1.2(Team, 2013)).

PD-MCI (0.702± 0.269) patients exhibited significantly lower accuracy on the motion discrimination task than HC (0.853 ± 0.241; p = 0.033) and PD-NC (0.880 ± 0.208; p =0.039). A Group * Coherence interaction was identified in which several regions, including orbitofrontal, posterior parietal and occipital cortex, showed increased activation during High relative to Low coherence trials in the PD patient groups but not in the HC group. HC showed default mode deactivation and frontal-parietal activation during Low relative to High coherence trials that was not evident in the patient groups.

PD-MCI patients exhibited worse visuospatial performance on a motion discrimination task than PD-NC and HC participants and exhibited hyperactivation of the posterior parietal and occipital regions during motion discrimination, suggesting possible compensatory activation.

In light of government and healthcare system commitments to reducing the carbon footprint of healthcare, health technology assessment (HTA) agencies are increasingly motivated to investigate how to consider environmental sustainability in their assessments and guidance. This constitutes a major departure from the existing remits and objectives of most agencies, which typically focus on improving population health outcomes. This presentation seeks to identify options for incorporating environmental impact data into HTA and to examine the main challenges, focusing on the National Institute for Health and Care Excellence (NICE) as a case study.

We present four broad approaches that could be pursued, informed by policy analysis undertaken by NICE. The strengths, weaknesses and implications of each approach are assessed.

The first option is to act as an ‘information conduit’, aggregating and distributing in a standardized format environmental impact information that is provided voluntarily by health technology manufacturers. The second is to present complementary analyses of environmental impact data, separately but alongside results from established health economic analyses (‘parallel evaluation’ model). The third is to incorporate environmental impact data into health economic analyses, for example by monetizing environmental outcomes, so that quantitative estimates of treatment value are directly affected by environmental benefits and costs (‘integrated evaluation’ model). The fourth is to create new decision-making frameworks for evaluating healthcare interventions that are not expected to improve health-related outcomes, but claim to have relative environmental benefits.

We conclude that these approaches are not mutually exclusive, and all involve some degree of benefit and risk. We explain why the parallel evaluation model may be the most appropriate approach for NICE as a first response to the increased demand for guidance on the environmental impact of health technologies. We also outline activities being undertaken by NICE and other agencies such as the Canadian Agency for Drugs and Technologies in Health to develop new methodologies for incorporating environmental impact data into their HTAs.