Colin Groot

The effect of physical activity on cognitive function in patients with dementia: A meta-analysis of randomized control trials.

Non-pharmacological therapies, such as physical activity interventions, are an appealing alternative or add-on to current pharmacological treatment of cognitive symptoms in patients with dementia. In this meta-analysis, we investigated the effect of physical activity interventions on cognitive function in dementia patients, by synthesizing data from 802 patients included in 18 randomized control trials that applied a physical activity intervention with cognitive function as an outcome measure. Post-intervention standardized mean difference (SMD) scores were computed for each study, and combined into pooled effect sizes using random effects meta-analysis. The primary analysis yielded a positive overall effect of physical activity interventions on cognitive function (SMD[95% confidence interval]=0.42[0.23;0.62], p<.01). Secondary analyses revealed that physical activity interventions were equally beneficial in patients with Alzheimers disease (AD, SMD=0.38[0.09;0.66], p<.01) and in patients with AD or a non-AD dementia diagnosis (SMD=0.47[0.14;0.80], p<.01). Combined (i.e. aerobic and non-aerobic) exercise interventions (SMD=0.59[0.32;0.86], p<.01) and aerobic-only exercise interventions (SMD=0.41[0.05;0.76], p<.05) had a positive effect on cognition, while this association was absent for non-aerobic exercise interventions (SMD=-0.10[-0.38;0.19], p=.51). Finally, we found that interventions offered at both high frequency (SMD=0.33[0.03;0.63], p<.05) and at low frequency (SMD=0.64[0.39;0.89], p<.01) had a positive effect on cognitive function. This meta-analysis suggests that physical activity interventions positively influence cognitive function in patients with dementia. This beneficial effect was independent of the clinical diagnosis and the frequency of the intervention, and was driven by interventions that included aerobic exercise.

A neuroimaging approach to capture cognitive reserve: Application to Alzheimer's disease

Cognitive reserve (CR) explains interindividual differences in the ability to maintain cognitive function in the presence of neuropathology. We developed a neuroimaging approach including a measure of brain atrophy and cognition to capture this construct. In a group of 511 Alzheimers disease (AD) biomarker‐positive subjects in different stages across the disease spectrum, we performed 3T magnetic resonance imaging and predicted gray matter (GM) volume in each voxel based on cognitive performance (i.e. a global cognitive composite score), adjusted for age, sex, disease stage, premorbid brain size (i.e. intracranial volume) and scanner type. We used standardized individual differences between predicted and observed GM volume (i.e. W‐scores) as an operational measure of CR. To validate this method, we showed that education correlated with mean W‐scores in whole‐brain (ru2009=u2009−0.090, P < 0.05) and temporoparietal (r = −0.122, P < 0.01) masks, indicating that higher education was associated with more CR (i.e. greater atrophy than predicted from cognitive performance). In a voxel‐wise analysis, this effect was most prominent in the right inferior and middle temporal and right superior lateral occipital cortex (P < 0.05, corrected for multiple comparisons). Furthermore, survival analyses among subjects in the pre‐dementia stage revealed that the W‐scores predicted conversion to more advanced disease stages (whole‐brain: hazard ratio [HR]u2009=u20090.464, P < 0.05; temporoparietal: HR = 0.397, P < 0.001). Our neuroimaging approach captures CR with high anatomical detail and at an individual level. This standardized method is applicable to various brain diseases or CR proxies and can flexibly incorporate different neuroimaging modalities and cognitive parameters, making it a promising tool for scientific and clinical purposes. Hum Brain Mapp 38:4703–4715, 2017.

Association of Amyloid Positron Emission Tomography With Changes in Diagnosis and Patient Treatment in an Unselected Memory Clinic Cohort: The ABIDE Project

Importance Previous studies have evaluated the diagnostic effect of amyloid positron emission tomography (PET) in selected research cohorts. However, these research populations do not reflect daily practice, thus hampering clinical implementation of amyloid imaging. Objective To evaluate the association of amyloid PET with changes in diagnosis, diagnostic confidence, treatment, and patients’ experiences in an unselected memory clinic cohort. Design, Setting, and Participants Amyloid PET using fluoride-18 florbetaben was offered to 866 patients who visited the tertiary memory clinic at the VU University Medical Center between January 2015 and December 2016 as part of their routine diagnostic dementia workup. Of these patients, 476 (55%) were included, 32 (4%) were excluded, and 358 (41%) did not participate. To enrich this sample, 31 patients with mild cognitive impairment from the University Medical Center Utrecht memory clinic were included. For each patient, neurologists determined a preamyloid and postamyloid PET diagnosis that existed of both a clinical syndrome (dementia, mild cognitive impairment, or subjective cognitive decline) and a suspected etiology (Alzheimer disease [AD] or non-AD), with a confidence level ranging from 0% to 100%. In addition, the neurologist determined patient treatment in terms of ancillary investigations, medication, and care. Each patient received a clinical follow-up 1 year after being scanned. Main Outcomes and Measures Primary outcome measures were post-PET changes in diagnosis, diagnostic confidence, and patient treatment. Results Of the 507 patients (mean [SD] age, 65 (8) years; 201 women [39%]; mean [SD] Mini-Mental State Examination score, 25 [4]), 164 (32%) had AD dementia, 70 (14%) non-AD dementia, 114 (23%) mild cognitive impairment, and 159 (31%) subjective cognitive decline. Amyloid PET results were positive for 242 patients (48%). The suspected etiology changed for 125 patients (25%) after undergoing amyloid PET, more often due to a negative (82 of 265 [31%]) than a positive (43 of 242 [18%]) PET result (Pu2009<u2009.01). Post-PET changes in suspected etiology occurred more frequently in patients older (>65 years) than younger (<65 years) than the typical age at onset of 65 years (74 of 257 [29%] vs 51 of 250 [20%]; Pu2009<u2009.05). Mean diagnostic confidence (SD) increased from 80 (13) to 89 (13%) (Pu2009<u2009.001). In 123 patients (24%), there was a change in patient treatment post-PET, mostly related to additional investigations and therapy. Conclusions and Relevance This prospective diagnostic study provides a bridge between validating amyloid PET in a research setting and implementing this diagnostic tool in daily clinical practice. Both amyloid-positive and amyloid-negative results had substantial associations with changes in diagnosis and treatment, both in patients with and without dementia.

Prevalence of the Apolipoprotein E ε4 allele in amyloid-β positive subjects across the spectrum of Alzheimer’s disease

Apolipoprotein E (APOE) ε4 is the major genetic risk factor for Alzheimers disease (AD), but its prevalence is unclear because earlier studies did not require biomarker evidence of amyloid β (Aβ) pathology.

Prevalence of amyloid-β pathology in distinct variants of primary progressive aphasia: Amyloid-β pathology in PPA variants

To estimate the prevalence of amyloid positivity, defined by positron emission tomography (PET)/cerebrospinal fluid (CSF) biomarkers and/or neuropathological examination, in primary progressive aphasia (PPA) variants.

Is intracranial volume a suitable proxy for brain reserve

BackgroundBrain reserve is a concept introduced to explain why Alzheimer’s disease (AD) patients with a greater brain volume prior to onset of pathology generally have better clinical outcomes. In this review, we provide a historical background of the emergence of brain reserve and discuss several aspects that need further clarification, including the dynamic or static nature of the concept and its underlying mechanisms and clinical effect. We then describe how brain reserve has been operationalized over the years, and critically evaluate the use of intracranial volume (ICV) as the most widely used proxy for brain reserve. Furthermore, we perform a meta-analysis showing that ICV is associated with higher cognitive performance after adjusting for the presence and amount of pathology. Although we acknowledge its imperfections, we conclude that the use of ICV as a proxy for brain reserve is currently warranted. However, further development of more optimal measures of brain reserve as well as a more clearly defined theoretical framework is essential.

IMPACT OF CO-MORBID AMYLOID PATHOLOGY ON CLINICAL PHENOTYPE OF PATIENTS WITH VASCULAR COGNITIVE DISORDERS

significantly more accurate than Tau in discriminating AD from MCI. Final analysis incorporating all studies up to 2015 will be presented. Conclusions: CSF biomarkers have good sensitivity and specificity, especially since most studies were based on clinically evaluated cohorts, meaning a proportion of controls will have preclinical AD, and some patients with non-AD dementia will have AD or concomitant AD pathology. CSF biomarker ratios have greater diagnostic accuracy than single biomarkers.

ACTIVE AND PASSIVE RESERVE DIFFERENTIALLY MITIGATE COGNITIVE SYMPTOMS IN DEMENTED AND NON-DEMENTED STAGES OF ALZHEIMER’S DISEASE

most strongly associated with the WCSS (Spearman r1⁄4-0.345, p1⁄40.027), but it was not associated with total WMH volume (r1⁄40.147, p1⁄40.358). This was confirmed in the NACC cohort (r1⁄4-0.247, p1⁄40.031 versus r1⁄4-0.03, p1⁄40.789). Conclusions: The WCSS (i) is independent of the total volume of WMHs, as the WCSS of an arbitrary number of spherical WMHs vanishes, (ii) is rater-independent, as it is computed fully automatically, and (iii) seems to be more strongly associated with cognitive performance in specific domains than the total WMH volume.

A NOVEL NEUROIMAGING APPROACH TO CAPTURE COGNITIVE RESERVE

0.4-2.9 years) while 28 remained stable. Conversion time in MCI-ADwas defined as both impairment in IADL andMMSE<24. FDG-PET of MCI-AD was compared (SPM8) with those from 48 age-matched healthy controls (CTR) to verify presence of ADpattern (age and education as nuisance). Multiple regression analysis in SPM8 was used to verify the correlation between conversion time and brain metabolism (progression-pattern). Age, gender, education, MMSE were included as nuisance. Each MCI-AD was individually compared with CTR and grouped according to presence of AD-pattern and progression-pattern. Kaplan-Meier analysis was performed to evaluate the capability of ADand progressionpatterns, respectively, to predict conversion time. Results: In MCIAD, AD-pattern corresponded to bilateral parietal cortex, posterior cingulate and precuneus while time to conversion correlated with hypometabolism in the right middle and inferior temporal gyri (BA 20, 21, 38) (p<0.05 FDR corrected at peak and uncorrected p<0.001 at cluster level). This latter correlation was still significant even after correction for the severity of hypometabolism in posterior AD-tipical regions as well as for whole-brain hypometabolism. In the Kaplan-Meier analysis, the presence of progression-pattern (not of AD-pattern) significantly distinguished early and late MCI-AD converters since baseline (p<0.007). These two subgroups of MCI-AD were not otherwise different with respect to age, education, baseline MMSE and neuropsychological test scores. Conclusions:As proposed by IWG2, PET is able to quantify time to meaningful disease milestones such as conversion time. However not the AD-typical posterior pattern but baseline temporal metabolism (BA 20-21-38) can predict conversion time in MCIAD. Brain metabolism in this region thus represents a further source of heterogeneity in MCI-AD and can affect a crucial endpoint of AD interventional trials.