Short communication: Lifetime musical activity and resting-state functional connectivity in cognitive networks

BACKGROUND

Participation in multimodal leisure activities, such as playing a musical instrument, may be protective against brain aging and dementia in older adults (OA). Potential neuroprotective correlates underlying musical activity remain unclear.

OBJECTIVE

This cross-sectional study investigated the association between lifetime musical activity and resting-state functional connectivity (RSFC) in three higher-order brain networks: the Default Mode, Fronto-Parietal, and Salience networks.

METHODS

We assessed 130 cognitively unimpaired participants (≥ 60 years) from the baseline cohort of the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) study. Lifetime musical activity was operationalized by the self-reported participation in musical instrument playing across early, middle, and late life stages using the Lifetime of Experiences Questionnaire (LEQ). Participants who reported musical activity during all life stages (n = 65) were compared to controls who were matched on demographic and reserve characteristics (including education, intelligence, socioeconomic status, self-reported physical activity, age, and sex) and never played a musical instrument (n = 65) in local (seed-to-voxel) and global (within-network and between-network) RSFC patterns using pre-specified network seeds.

RESULTS

Older participants with lifetime musical activity showed significantly higher local RSFC between the medial prefrontal cortex (Default Mode Network seed) and temporal as well as frontal regions, namely the right temporal pole and the right precentral gyrus extending into the superior frontal gyrus, compared to matched controls. There were no significant group differences in global RSFC within or between the three networks.

CONCLUSION

We show that playing a musical instrument during life relates to higher RSFC of the medial prefrontal cortex with distant brain regions involved in higher-order cognitive and motor processes. Preserved or enhanced functional connectivity could potentially contribute to better brain health and resilience in OA with a history in musical activity.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS00007966, 04/05/2015).

Machine Learning-Based Perivascular Space Volumetry in Alzheimer Disease

OBJECTIVES

Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD.

MATERIALS AND METHODS

A convolutional neural network was trained using manually corrected, filter-based segmentations (n = 1000) to automatically segment the PVS in the centrum semiovale from interpolated, coronal T2-weighted magnetic resonance imaging scans (n = 894). These scans were sourced from the national German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study. Convolutional neural network-based segmentations and those performed by a human rater were compared in terms of segmentation volume, identified PVS clusters, as well as Dice score. The comparison revealed good segmentation quality (Pearson correlation coefficient r = 0.70 with P < 0.0001 for PVS volume, detection rate in cluster analysis = 84.3%, and Dice score = 59.0%). Subsequent multivariate linear regression analysis, adjusted for participants' age, was performed to correlate PVS volume with clinical diagnoses, disease progression, cerebrospinal fluid biomarkers, lifestyle factors, and cognitive function. Cognitive function was assessed using the Mini-Mental State Examination, the Comprehensive Neuropsychological Test Battery, and the Cognitive Subscale of the 13-Item Alzheimer's Disease Assessment Scale.

RESULTS

Multivariate analysis, adjusted for age, revealed that participants with AD and MCI, but not those with SCD, had significantly higher PVS volumes compared with CU participants without SCD (P = 0.001 for each group). Furthermore, CU participants who developed incident MCI within 4.5 years after the baseline assessment showed significantly higher PVS volumes at baseline compared with those who did not progress to MCI (P = 0.03). Cognitive function was negatively correlated with PVS volume across all participant groups (P ≤ 0.005 for each). No significant correlation was found between PVS volume and any of the following parameters: cerebrospinal fluid biomarkers, sleep quality, body mass index, nicotine consumption, or alcohol abuse.

CONCLUSIONS

The very early changes of PVS volume may suggest that alterations in PVS function are involved in the pathophysiology of AD. Overall, the volumetric assessment of centrum semiovale PVS represents a very early imaging biomarker for AD.

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aβ42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.

Subjective cognitive decline and stage 2 of Alzheimer disease in patients from memory centers

INTRODUCTION

It is uncertain whether subjective cognitive decline (SCD) in individuals who seek medical help serves the identification of the initial symptomatic stage 2 of the Alzheimer's disease (AD) continuum.

METHODS

Cross-sectional and longitudinal data from the multicenter, memory clinic-based DELCODE study.

RESULTS

The SCD group showed slightly worse cognition as well as more subtle functional and behavioral symptoms than the control group (CO). SCD-A+ cases (39.3% of all SCD) showed greater hippocampal atrophy, lower cognitive and functional performance, and more behavioral symptoms than CO-A+. Amyloid concentration in the CSF had a greater effect on longitudinal cognitive decline in SCD than in the CO group.

DISCUSSION

Our data suggests that SCD serves the identification of stage 2 of the AD continuum and that stage 2, operationalized as SCD-A+, is associated with subtle, but extended impact of AD pathology in terms of neurodegeneration, symptoms and clinical progression.

Which features of subjective cognitive decline are related to amyloid pathology? Findings from the DELCODE study

Background

Subjective cognitive decline (SCD) has been proposed as a pre-MCI at-risk condition of Alzheimer’s disease (AD). Current research is focusing on a refined assessment of specific SCD features associated with increased risk for AD, as proposed in the SCD-plus criteria. We developed a structured interview (SCD-I) for the assessment of these features and tested their relationship with AD biomarkers.

Methods

We analyzed data of 205 cognitively normal participants of the DELCODE study (mean age = 68.9 years; 52% female) with available CSF AD biomarkers (Aß-42, p-Tau181, Aß-42/Tau ratio, total Tau). For each of five cognitive domains (including memory, language, attention, planning, others), a study physician asked participants about the following SCD-plus features: the presence of subjective decline, associated worries, onset of SCD, feeling of worse performance than others of the same age group, and informant confirmation. We compared AD biomarkers of subjects endorsing each of these questions with those who did not, controlling for age. SCD was also quantified by two summary scores: the number of fulfilled SCD-plus features, and the number of domains with experienced decline. Covariate-adjusted linear regression analyses were used to test whether these SCD scores predicted abnormality in AD biomarkers.

Results

Lower Aß-42 levels were associated with a reported decline in memory and language abilities, and with the following SCD-plus features: onset of subjective decline within 5 years, confirmation of cognitive decline by an informant, and decline-related worries. Furthermore, both quantitative SCD scores were associated with lower Aß42 and lower Aß42/Tau ratio, but not with total Tau or p-Tau181.

Conclusions

Findings support the usefulness of a criterion-based interview approach to assess and quantify SCD in the context of AD and validate the current SCD-plus features as predictors of AD pathology. While some features seem to be more closely associated with AD biomarkers than others, aggregated scores over several SCD-plus features or SCD domains may be the best predictors of AD pathology.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0515-y) contains supplementary material, which is available to authorized users.

Structural integrity in subjective cognitive decline, mild cognitive impairment and Alzheimer's disease based on multicenter diffusion tensor imaging

INTRODUCTION

Subjective cognitive decline (SCD) can represent a preclinical stage of Alzheimer's disease. Diffusion tensor imaging (DTI) could aid an early diagnosis, yet only few monocentric DTI studies in SCD have been conducted, reporting heterogeneous results. We investigated microstructural changes in SCD in a larger, multicentric cohort.

METHODS

271 participants with SCD, mild cognitive impairment (MCI) or Alzheimer's dementia (AD) and healthy controls (CON) were included, recruited prospectively at nine centers of the observational DELCODE study. DTI was acquired using identical protocols. Using voxel-based analyses, we investigated fractional anisotropy (FA), mean diffusivity (MD) and mode (MO) in the white matter (WM). Discrimination accuracy was determined by cross-validated elastic-net penalized regression. Center effects were explored using variance analyses.

RESULTS

MO and FA were lower in SCD compared to CON in several anterior and posterior WM regions, including the anterior corona radiata, superior and inferior longitudinal fasciculus, cingulum and splenium of the corpus callosum (p < 0.01, uncorrected). MD was higher in the superior and inferior longitudinal fasciculus, cingulum and superior corona radiata (p < 0.01, uncorrected). The cross-validated accuracy for discriminating SCD from CON was 67% (p < 0.01). As expected, the AD and MCI groups had higher MD and lower FA and MO in extensive regions, including the corpus callosum and temporal brain regions. Within these regions, center accounted for 3-15% of the variance.

CONCLUSIONS

DTI revealed subtle WM alterations in SCD that were intermediate between those in MCI and CON and may be useful to detect individuals with an increased risk for AD in clinical studies.

Environmental factors in early childhood are associated with multiple sclerosis: a case-control study

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS) with increasing incidence mainly in high-income countries. One explanation of this phenomenon may be a higher prevalence of allergic and autoimmune diseases in industrialized countries as a consequence of otherwise beneficial advances in sanitation (hygiene hypothesis). We investigated environmental factors in early childhood associated with MS.

METHODS

A case-control study was performed of 245 MS patients and 296 population-based controls in Berlin. The study participants completed a standardized questionnaire on environmental factors in childhood and youth, including aspects of personal and community hygiene. Multivariable logistic regression analysis was performed to investigate factors in childhood and youth associated with the occurrence of MS.

RESULTS

Mean age was 46 years (range, 20-80) in the MS group and 42 years (range 18-80) in the control group, of which 73.9% in the MS and 61.5% in the control group were female. The multivariable analysis showed that having at least two older siblings (OR 0.54; p = 0.05, for individuals with two older siblings compared to individuals without older siblings), attending a day-care center (OR 0.5; p = 0.004) and growing up in an urban center with more than 100, 000 inhabitants (OR 0.43; p = 0.009) were factors independently associated with a lower chance for MS.

CONCLUSIONS

The hygiene hypothesis may play a role in the occurrence of MS and could explain disease distribution and increasing incidence.

Ultrasonographic measurement of cerebral blood flow, cerebral circulation time and cerebral blood volume in vascular and Alzheimer's dementia

Vascular dementia (VD) and Alzheimer's dementia (AD) are the most common differential diagnoses in patients with cognitive impairment. Although of different etiology, small vessel disease is postulated to be present in both conditions. We investigated global cerebral blood flow (CBF), global cerebral circulation time (CCT) and global cerebral blood volume (CBV) in VD and AD patients using a multimodal ultrasound (US) approach. 20 VD and 20 AD patients were included and compared with 12 age-matched controls. Duplex US of both internal carotid and vertebral arteries was performed to measure CBF. CCT was defined as the time delay of an echo-contrast bolus arrival between the internal carotid artery and internal jugular vein using extracranial Doppler. CBV was calculated as the product of CBF and CCT. CBF was significantly lower (VD: 570 +/- 61; AD: 578 +/- 77; controls: 733 +/- 54 ml/min) and CCT significantly longer (8.8 +/- 2.6; 8.2 +/- 1.4; 6.4 +/- 0.8 s) in both patient groups compared with controls (p < 0.003). No difference in CBF and CCT was found between the two patient groups. CBV was similar in all three groups (82 +/- 20; 79 +/- 19; 78 +/- 9 ml). The equally reduced CBF and prolonged CCT in VD and AD support the hypothesis, that small vessel disease is a relevant factor in both types of dementia. The presented multimodal US approach helps to assess the extent of changes in the global cerebral hemodynamics in patients with dementia but does not allow a differentiation between VD and AD.

Expression of ACE mRNA in the human atrial myocardium is not dependent on left ventricular function, ACE inhibitor therapy, or the ACE I/D genotype

The activity of the cardiac renin-angiotensin system is altered in human heart failure, but the regulatory mechanisms are unknown. We analyzed whether angiotensin-converting enzyme (ACE) mRNA expression in heart failure is altered in the atrial myocardium, and whether a correlation exists between atrial ACE mRNA expression and the parameters of left ventricular function. We also investigated whether the use of ACE inhibitors or the ACE I/D genotype modulates the atrial ACE mRNA content. For this purpose patients who were to undergo routine cardiac surgery were selected in a prospective manner according to left ventricular function and ACE inhibitor therapy. Samples of atrial myocardium were taken, and ACE mRNA expression was determined by internally standardized reverse transcription polymerase chain reaction. Atrial ACE mRNA expression did not differ in patients with left ventricular ejection fraction higher than 55% (2423+/-199 copies/ng RNA) and those with a value less than 45% (2661+/-143 copies/ng RNA, n.s.). ACE mRNA expression also did not differ in patients using ACE inhibitors (2585+/-175 copies/ng RNA) and those not using ACE inhibitors (2476+/-185 copies/ng RNA). Furthermore, atrial ACE mRNA expression was not affected by the ACE genotype (DD 2573+/-203, ID 2472+/-215, II 2563+/-249 copies/ng RNA). We conclude that the regulation of atrial ACE mRNA expression occurs predominantly by local mechanical or para- or autocrine factors.