Citations

AbstractBackgroundSubjective cognitive decline (SCD) implies self‐reported cognitive complaints in absence of objective cognitive impairment. SCD has been postulated as an early marker of Alzheimer’s Disease (AD). However, longitudinal studies have linked SCD to future vascular dementia. Thus, we conducted a systematic review and meta‐analysis aiming to synthesise and analyse the available data on the association between SCD and the presence of underlying cerebrovascular pathology measured by MRI.MethodOur study was performed following the PRISMA statement. The search strategy included terms for “subjective cognitive decline” and “white matter signal abnormalities”. It was conducted in 3 databases (PubMed, Scopus and Web of Science) from origin by December 8th, 2021. Two authors screened the studies in a 2‐stepwise approach. Primary studies including cognitively unimpaired adults with subjective cognitive complaints meeting the SCD‐I criteria for SCD that evaluated cerebrovascular pathology based on white matter signal abnormalities (WMSA) on MR images were selected. Study quality was assessed using the FLC 3.0 tool. Meta‐analysis comparing case‐control mean difference of WMSA was performed.ResultOf 240 articles identified, 16 research articles were selected. Of those, 8 case‐control studies were selected for the meta‐analysis. The sample size of the SCD group across the studies ranged from 8 to 906 participants, with age ranged from 40 to 84 years. All eight studies comprised a total of 2.637 SCD individuals. Study quality was deemed moderate (n=6) to high (n=10). The meta‐analysis showed a significant overall effect‐size between the mean WMSA burden of SCD and the controls, that was not significantly heterogeneous (Q(gl=7)=14.20; p=0,04; I2=42,5%). SCD was significantly associated with higher CVP burden based on neuroimaging measures.ConclusionThis is the first study to synthesise and analyse the current evidence on the association between SCD and WMSA. Our findings show the usefulness of SCD as a marker of underlying cerebrovascular pathology. Thus, the potential role of SCD as a preclinical stage of vascular cognitive impairment needs further investigation. Changes in lifestyle (e.g. exercise, diet, smoking cessation), cognitive stimulation or pharmacotherapy may be key to avoid an evolution towards cognitive deterioration in SCD.

AbstractBackgroundSeveral hypotheses exist regarding the neurologic substrate underlying visual hallucinations (VH) in Dementia with Lewy Bodies (DLB). Studies have suggested an association with regional cerebral hypometabolism in the primary visual or visual association cortices on FDG‐PET. We compared regional cerebral glucose metabolism and posterior cingulate island sign (CIS) ratios in DLB and in Mild Cognitive Impairment with Lewy Bodies (MCI‐LB) participants with (VH+) and without (VH‐) VH.MethodParticipants with clinically probable DLB or MCI‐LB and an FDG‐PET scan were identified from the Mayo ADRC database. Responses on the Visual Hallucinations and Delusions Questionnaire and Neuropsychiatric Inventory were used to assess presence or absence of VH. Standardized uptake value ratios (SUVr) were used to calculate regional cerebral glucose hypometabolism in the primary visual cortex (PVC), lateral occipital cortex (LOC), and whole occipital cortex (WOC). The CIS ratio was defined as posterior cingulate divided by the sum of the precuneus plus cuneus FDG‐PET uptake ratio.ResultTwo cohorts were identified (27 DLB and 18 MCI‐LB, 87% male). Eighteen DLB participants were VH+ and 9 were VH‐, whereas 5 MCI‐LB were VH+ and 13 were VH‐. There were no statistically significant differences in demographic (age, sex, education) and clinical features (MoCA, CDR, presence of other core DLB features, and UPDRS) between VH+ vs VH‐ in those with DLB, and between VH+ vs VH‐ in those with MCI‐LB. Similarly, there were no statistically significant differences in DLB between VH+ vs VH‐ in PVC (1.55±0.14 vs 1.58±0.15), LOC (1.22±0.16 vs 1.28±0.12), WOC (1.29±0.15 vs 1.34±0.11), or CIS ratio (1.16±0.08 vs 1.14±0.10), nor in MCI‐LB between VH+ vs VH‐ in PVC (1.68±0.19 vs 1.58±0.14), LOC (1.40±0.21 vs 1.32±0.14), WOC (1.45±0.20 vs 1.37±0.14), or CIS ratio (1.17±0.08 vs 1.19±0.10) (P>0.05 for all comparisons).ConclusionThese findings do not support a direct correlation between regional cerebral glucose metabolism and VH in DLB or MCI‐LB. Further work in DLB and MCI‐LB with larger numbers is warranted. Other neurologic substrates and mechanisms, such as REM sleep intrusions during wakefulness, neurotransmitter dysfunction, and disruption of functional connectivity across brain regions should be explored as contributors to VH in DLB.

AbstractBackgroundSubjective cognitive decline (SCD) in cognitively normal individuals is a relevant predictor of future cognitive decline, dementia, and neurodegeneration. Previous studies showed that the cognitive decline cannot be captured by objective neuropsychological testing in SCD. However, whether sophisticated multivariate approaches such as graph theory can potentially capture subtle cognitive abnormalities in SCD individuals is unknown. This study aimed to assess the cognitive connectome in a relatively large cohort of SCD individuals, in comparison with healthy controls (HC), and assess whether such a connectome differs in SCD across age groups.MethodWe included a total of 390 individuals, of which 211 fulfilled the core SCD criteria proposed by the international SCD Initiative (SCD‐I), and 179 individuals were classified as healthy controls. Participants were stratified into early middle‐age (37–50 years, n = 125), late‐middle‐age (51–64 years, n = 119), and elderly (65–78 years, n = 146) groups. We built cognitive networks from 47 cognitive variables separately for each age and study group using graph theory and compared the groups using global measures of average strength, average global efficiency, average local efficiency, and transitivity.ResultSCD individuals had more segregated networks in all age groups, but more prominently in the late‐middle‐age group. We observed lower within‐module connectivity in SCD individuals, in verbal memory and executive—premotor functions compared with HC. The late middle‐age SCD group showed a pattern of connectivity similar to the elderly HC group. The late‐middle‐age SCD group also showed reduced transitivity compared with the HC. We did not observe significant differences in the rest of the graph measures and in the other groups.ConclusionUsing a sophisticated multivariate approach, we demonstrate that a segregated pattern of cognitive connectivity seems to underlie SCD, particularly in individuals in the age range 51 to 64 years. After the age of 65 years, this pattern tends to be less segregated, contrary to an efficient dedifferentiation process in aging. This, together with subtle signs of brain pathology and other risk factors identified in SCD may increase the likelihood of future cognitive impairment and dementia.

Abstract Background. Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. Objectives. We used data-driven analysis of MRI data to investigate DLB subtypes. Methods. We included 165 probable DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid and cerebrovascular biomarkers at baseline, and cognitive decline over three years. Results. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n=49, 30%); a subtype with low GM volumes in fronto-occipital regions (n=76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n=40, 24%). Conclusions. This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

1011 Background: Several trials have shown 25-30% pathologic complete response (pCR) rates in patients with HER2+ BC treated with LT therapy (+ endocrine therapy if ER+), but no chemotherapy (CTX). We hypothesize that a multiparameter classifier, comprised of HER2 gene and protein levels, intratumor heterogeneity (ITH), HER2-enriched (E) subtype, and PIK3CA mutation status can identify patients whose tumors are “addicted” to HER2 signaling and are likely to achieve pCR from a CTX-sparing de-escalation strategy. Methods: Baseline specimens from 2 trials (TBCRC023 [NCT00999804] and PAMELA [NCT01973660]) of neoadjuvant CTX-sparing LT (+ endocrine therapy if ER+) in HER2+ BC were used. HER2 protein and ITH (scored for % 3+ by IHC), and gene amplification (HER2:CEP17 ratio and copy number (CN) by CISH) were measured on the same slide by the dual gene protein assay (GPA). HER2-E and PIK3CA mutation status were assessed by research-based PAM50 and MSK-IMPACT platforms, respectively. A decision tree algorithm was used to determine the GPA cutoffs and to construct the classifier of response (by pCR) in TBCRC023, which was then validated in PAMELA. Results: Of the evaluable patients from TBCRC023 (N = 130) and PAMELA (N = 151), GPA data were available for 121 and 94 cases, respectively. Both cohorts exhibited similar distributions for HER2 ratio, CN, and % 3+, and a strong correlation between HER2 ratio and CN (R > 0.92). In TBCRC023, 73 cases had data from GPA, PAM50, and IMPACT, of which 15 had pCR. Recursive partitioning identified cutoffs of HER2 ratio > 4.6 and % 3+ > 97.5% in both the GPA data cohort (N = 121) and complete data cohort (N = 73). With PAM50 and IMPACT data, the model added HER2-E and PIK3CA wild-type (wt). For practical reasons, the classifier was locked as HER2 ratio ≥ 4.5 AND % 3+ ≥ 90% AND PIK3CA-wt AND HER2-E, which yielded a PPV of 55% and NPV of 94%. Validation in PAMELA using 45 cases with data for all 3 assays yielded PPV of 44% and NPV of 82%. 29 TBCRC023 cases without IMPACT data could be predicted to be non-pCR, of which 26 were correct (NPV = 89%). In PAMELA, 66 additional cases could be predicted to be non-pCR, of which 54 were correct (NPV = 81%). Conclusions: We have constructed a multiparameter classifier that can predict pCR with targeted therapy alone that compare to pCR rates of CTX + dual anti-HER2 in unselected patients. Prospective validation in a clinical trial is warranted. [Table: see text]