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A symptom of mild cognitive impairment (MCI) and Alzheimer’s disease
(AD) is a flat learning profile. Learning slope calculation methods vary, and
the optimal method for capturing neuroanatomical changes associated with MCI and
early AD pathology is unclear. This study cross-sectionally compared four
different learning slope measures from the Rey Auditory Verbal Learning Test
(simple slope, regression-based slope, two-slope method, peak slope) to
structural neuroimaging markers of early AD neurodegeneration (hippocampal
volume, cortical thickness in parahippocampal gyrus, precuneus, and lateral
prefrontal cortex) across the cognitive aging spectrum [normal
control (NC); (n=198;
age=76±5), MCI (n=370;
age=75±7), and AD (n=171;
age=76±7)] in ADNI. Within diagnostic group,
general linear models related slope methods individually to neuroimaging
variables, adjusting for age, sex, education, and APOE4 status. Among MCI,
better learning performance on simple slope, regression-based slope, and late
slope (Trial 2–5) from the two-slope method related to larger
parahippocampal thickness (all p-values<.01) and
hippocampal volume (p<.01). Better regression-based
slope (p<.01) and late slope
(p<.01) were related to larger ventrolateral
prefrontal cortex in MCI. No significant associations emerged between any slope
and neuroimaging variables for NC (p-values ≥.05) or
AD (p-values ≥.02). Better learning performances
related to larger medial temporal lobe (i.e., hippocampal volume,
parahippocampal gyrus thickness) and ventrolateral prefrontal cortex in MCI
only. Regression-based and late slope were most highly correlated with
neuroimaging markers and explained more variance above and beyond other common
memory indices, such as total learning. Simple slope may offer an acceptable
alternative given its ease of calculation. (JINS, 2015,
Many adolescents with mental health problems experience transition of care from child and adolescent mental health services (CAMHS) to adult mental health services (AMHS).
As part of the TRACK study we evaluated the process, outcomes and user and carer experience of transition from CAMHS to AMHS.
We identified a cohort of service users crossing the CAMHS/AMHS boundary over 1 year across six mental health trusts in England. We tracked their journey to determine predictors of optimal transition and conducted qualitative interviews with a subsample of users, their carers and clinicians on how transition was experienced.
Of 154 individuals who crossed the transition boundary in 1 year, 90 were actual referrals (i.e. they made a transition to AMHS), and 64 were potential referrals (i.e. were either not referred to AMHS or not accepted by AMHS). Individuals with a history of severe mental illness, being on medication or having been admitted were more likely to make a transition than those with neurodevelopmental disorders, emotional/neurotic disorders and emerging personality disorder. Optimal transition, defined as adequate transition planning, good information transfer across teams, joint working between teams and continuity of care following transition, was experienced by less than 5% of those who made a transition. Following transition, most service users stayed engaged with AMHS and reported improvement in their mental health.
For the vast majority of service users, transition from CAMHS to AMHS is poorly planned, poorly executed and poorly experienced. The transition process accentuates pre-existing barriers between CAMHS and AMHS.
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