Identifying key multi-modal predictors of incipient dementia in Parkinson’s disease: a machine learning analysis and Tree SHAP interpretation

BackgroundPersons with Parkinson’s disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.MethodParticipants were 48 well-characterized PD patients (Mbaseline age = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation.ResultsAn excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains.ConclusionOur data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains

Subcortical volumes in cerebral amyloid angiopathy compared with Alzheimer’s disease and controls

BackgroundPrevious reports have suggested that patients with cerebral amyloid angiopathy (CAA) may harbor smaller white matter, basal ganglia, and cerebellar volumes compared to age-matched healthy controls (HC) or patients with Alzheimer’s disease (AD). We investigated whether CAA is associated with subcortical atrophy.MethodsThe study was based on the multi-site Functional Assessment of Vascular Reactivity cohort and included 78 probable CAA (diagnosed according to the Boston criteria v2.0), 33 AD, and 70 HC. Cerebral and cerebellar volumes were extracted from brain 3D T1-weighted MRI using FreeSurfer (v6.0). Subcortical volumes, including total white matter, thalamus, basal ganglia, and cerebellum were reported as proportion (%) of estimated total intracranial volume. White matter integrity was quantified by the peak width of skeletonized mean diffusivity.ResultsParticipants in the CAA group were older (74.0 ± 7.0, female 44%) than the AD (69.7 ± 7.5, female 42%) and HC (68.8 ± 7.8, female 69%) groups. CAA participants had the highest white matter hyperintensity volume and worse white matter integrity of the three groups. After adjusting for age, sex, and study site, CAA participants had smaller putamen volumes (mean differences, −0.024% of intracranial volume; 95% confidence intervals, −0.041% to −0.006%; p = 0.005) than the HCs but not AD participants (−0.003%; −0.024 to 0.018%; p = 0.94). Other subcortical volumes including subcortical white matter, thalamus, caudate, globus pallidus, cerebellar cortex or cerebellar white matter were comparable between all three groups.ConclusionIn contrast to prior studies, we did not find substantial atrophy of subcortical volumes in CAA compared to AD or HCs, except for the putamen. Differences between studies may reflect heterogeneity in CAA presenting syndromes or severity

Brain iron content in cerebral amyloid angiopathy using quantitative susceptibility mapping

IntroductionCerebral amyloid angiopathy (CAA) is a small vessel disease that causes covert and symptomatic brain hemorrhaging. We hypothesized that persons with CAA would have increased brain iron content detectable by quantitative susceptibility mapping (QSM) on magnetic resonance imaging (MRI), and that higher iron content would be associated with worse cognition.MethodsParticipants with CAA (n = 21), mild Alzheimer’s disease with dementia (AD-dementia; n = 14), and normal controls (NC; n = 83) underwent 3T MRI. Post-processing QSM techniques were applied to obtain susceptibility values for regions of the frontal and occipital lobe, thalamus, caudate, putamen, pallidum, and hippocampus. Linear regression was used to examine differences between groups, and associations with global cognition, controlling for multiple comparisons using the false discovery rate method.ResultsNo differences were found between regions of interest in CAA compared to NC. In AD, the calcarine sulcus had greater iron than NC (β = 0.99 [95% CI: 0.44, 1.53], q < 0.01). However, calcarine sulcus iron content was not associated with global cognition, measured by the Montreal Cognitive Assessment (p > 0.05 for all participants, NC, CAA, and AD).DiscussionAfter correcting for multiple comparisons, brain iron content, measured via QSM, was not elevated in CAA compared to NC in this exploratory study

Gait in Cerebral Amyloid Angiopathy

Background Gait is a complex task requiring coordinated efforts of multiple brain networks. To date, there is little evidence on whether gait is altered in cerebral amyloid angiopathy (CAA). We aimed to identify impairments in gait performance and associations between gait impairment and neuroimaging markers of CAA, cognition, and falls. Methods and Results Gait was assessed using the Zeno Walkway during preferred pace and dual task walks, and grouped into gait domains (Rhythm, Pace, Postural Control, and Variability). Participants underwent neuropsychological testing and neuroimaging. Falls and fear of falling were assessed through self‐report questionnaires. Gait domain scores were standardized and analyzed using linear regression adjusting for age, sex, height, and other covariates. Participants were patients with CAA (n=29), Alzheimer disease with mild dementia (n=16), mild cognitive impairment (n=24), and normal elderly controls (n=47). CAA and Alzheimer disease had similarly impaired Rhythm, Pace, and Variability, and higher dual task cost than normal controls or mild cognitive impairment. Higher Pace score was associated with better global cognition, processing speed, and memory. Gait measures were not correlated with microbleed count or white matter hyperintensity volume. Number of falls was not associated with gait domain scores, but participants with low fear of falling had higher Pace (odds ratio [OR], 2.61 [95% CI, 1.59–4.29]) and lower Variability (OR, 1.64 [95% CI, 1.10–2.44]). Conclusions CAA is associated with slower walking, abnormal rhythm, and greater gait variability than in healthy controls. Future research is needed to identify the mechanisms underlying gait impairments in CAA, and whether they predict future falls