Screening for Cognitive Impairment by Model-Assisted Cerebral Blood Flow Estimation

OBJECTIVE

Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disease; a major health concern in the ageing population with an estimated prevalence of 46 million dementia cases worldwide. Early diagnosis is therefore crucial so mitigating treatments can be initiated at an early stage. Cerebral hypoperfusion has been linked with blood-brain barrier dysfunction in the early stages of AD, and screening for chronic cerebral hypoperfusion in individuals has been proposed for improving the early diagnosis of AD. However, ambulatory measurements of cerebral blood flow are not routinely carried out in the clinical setting. In this study, we combine physiological modeling with Holter blood pressure monitoring and carotid ultrasound imaging to predict 24-h cerebral blood flow (CBF) profiles in individuals. One hundred and three participants [53 with mild cognitive impairment (MCI) and 50 healthy controls] underwent model-assisted prediction of 24-h CBF. Model-predicted CBF and neuropsychological tests were features in lasso regression models for MCI diagnosis.

RESULTS

A CBF-enhanced classifier for diagnosing MCI performed better, area-under-the-curve (AUC) = 0.889 (95%-CI: 0.800 to 0.978), than a classifier based only on the neuropsychological test scores, AUC = 0.818 (95%-CI: 0.643 to 0.992). An additional cohort of 25 participants (11 MCI and 14 healthy) was recruited to perform model validation by arterial spin-labeling magnetic resonance imaging, and to establish a link between measured CBF that predicted by the model.

CONCLUSION

Ultrasound imaging and ambulatory blood pressure measurements enhanced with physiological modeling can improve MCI diagnosis accuracy.

Is Vasomotion in Cerebral Arteries Impaired in Alzheimer's Disease?

A substantial body of evidence supports the hypothesis of a vascular component in the pathogenesis of Alzheimer’s disease (AD). Cerebral hypoperfusion and blood-brain barrier dysfunction have been indicated as key elements of this pathway. Cerebral amyloid angiopathy (CAA) is a cerebrovascular disorder, frequent in AD, characterized by the accumulation of amyloid-β (Aβ) peptide in cerebral blood vessel walls. CAA is associated with loss of vascular integrity, resulting in impaired regulation of cerebral circulation, and increased susceptibility to cerebral ischemia, microhemorrhages, and white matter damage. Vasomotion— the spontaneous rhythmic modulation of arterial diameter, typically observed in arteries/arterioles in various vascular beds including the brain— is thought to participate in tissue perfusion and oxygen delivery regulation. Vasomotion is impaired in adverse conditions such as hypoperfusion and hypoxia. The perivascular and glymphatic pathways of Aβ clearance are thought to be driven by the systolic pulse. Vasomotion produces diameter changes of comparable amplitude, however at lower rates, and could contribute to these mechanisms of Aβ clearance. In spite of potential clinical interest, studies addressing cerebral vasomotion in the context of AD/CAA are limited. This study reviews the current literature on vasomotion, and hypothesizes potential paths implicating impaired cerebral vasomotion in AD/CAA. Aβ and oxidative stress cause vascular tone dysregulation through direct effects on vascular cells, and indirect effects mediated by impaired neurovascular coupling. Vascular tone dysregulation is further aggravated by cholinergic deficit and results in depressed cerebrovascular reactivity and (possibly) impaired vasomotion, aggravating regional hypoperfusion and promoting further Aβ and oxidative stress accumulation.

Protective Role of False Tendon in Subjects with Left Bundle Branch Block: A Virtual Population Study

False tendons (FTs) are fibrous or fibromuscular bands that can be found in both the normal and abnormal human heart in various anatomical forms depending on their attachment points, tissue types, and geometrical properties. While FTs are widely considered to affect the function of the heart, their specific roles remain largely unclear and unexplored. In this paper, we present an in silico study of the ventricular activation time of the human heart in the presence of FTs. This study presents the first computational model of the human heart that includes a FT, Purkinje network, and papillary muscles. Based on this model, we perform simulations to investigate the effect of different types of FTs on hearts with the electrical conduction abnormality of a left bundle branch block (LBBB). We employ a virtual population of 70 human hearts derived from a statistical atlas, and run a total of 560 simulations to assess ventricular activation time with different FT configurations. The obtained results indicate that, in the presence of a LBBB, the FT reduces the total activation time that is abnormally augmented due to a branch block, to such an extent that surgical implant of cardiac resynchronisation devices might not be recommended by international guidelines. Specifically, the simulation results show that FTs reduce the QRS duration at least 10 ms in 80% of hearts, and up to 45 ms for FTs connecting to the ventricular free wall, suggesting a significant reduction of cardiovascular mortality risk. In further simulation studies we show the reduction in the QRS duration is more sensitive to the shape of the heart then the size of the heart or the exact location of the FT. Finally, the model suggests that FTs may contribute to reducing the activation time difference between the left and right ventricles from 12 ms to 4 ms. We conclude that FTs may provide an alternative conduction pathway that compensates for the propagation delay caused by the LBBB. Further investigation is needed to quantify the clinical impact of FTs on cardiovascular mortality risk.

Vascular dysfunction in the pathogenesis of Alzheimer's disease--A review of endothelium-mediated mechanisms and ensuing vicious circles

Late-onset dementia is a major health concern in the ageing population. Alzheimer's disease (AD) accounts for the largest proportion (65-70%) of dementia cases in the older population. Despite considerable research effort, the pathogenesis of late-onset AD remains unclear. Substantial evidence suggests that the neurodegenerative process is initiated by chronic cerebral hypoperfusion (CCH) caused by ageing and cardiovascular conditions. CCH causes reduced oxygen, glucose and other nutrient supply to the brain, with direct damage not only to the parenchymal cells, but also to the blood-brain barrier (BBB), a key mediator of cerebral homeostasis. BBB dysfunction mediates the indirect neurotoxic effects of CCH by promoting oxidative stress, inflammation, paracellular permeability, and dysregulation of nitric oxide, a key regulator of regional blood flow. As such, BBB dysfunction mediates a vicious circle in which cerebral perfusion is reduced further and the neurodegenerative process is accelerated. Endothelial interaction with pericytes and astrocytes could also play a role in the process. Reciprocal interactions between vascular dysfunction and neurodegeneration could further contribute to the development of the disease. A comprehensive overview of the complex scenario of interacting endothelium-mediated processes is currently lacking, and could prospectively contribute to the identification of adequate therapeutic interventions. This study reviews the current literature of in vitro and ex vivo studies on endothelium-mediated mechanisms underlying vascular dysfunction in AD pathogenesis, with the aim of presenting a comprehensive overview of the complex network of causative relationships. Particular emphasis is given to vicious circles which can accelerate the process of neurovascular degeneration.

Effect of respiration on Korotkoff sounds and oscillometric cuff pressure pulses during blood pressure measurement

Blood pressure (BP) measurement accuracy depends on consistent changes in Korotkoff sounds (KorS) for manual measurement and oscillometric pulses for automated measurement, yet little is known about the direct effect of respiration on these physiological signals. The aim of this research was to quantitatively assess the modulation effect of respiration on Korotkoff sounds and oscillometric pulses. Systolic and diastolic blood pressures were measured manually from 30 healthy subjects (age 41 ± 12 years). Three static cuff pressure conditions were studied for two respiratory rates. Cuff pressure [with oscillometric pulses (OscP)], ECG, chest motion respiration [respiration signal (Resp), from magnetometer] and Korotkoff sounds (KorS, from digital stethoscope) were recorded twice for 20 s. The physiological data were evenly resampled. Respiratory frequency was calculated from Resp (f_R), OscP (f_O) and KorS (f_K) from peak spectral frequency. There was no statistically significant difference between f_R and f_O or f_K. Respiratory modulation was observed in all subjects. OscP amplitude modulation changed significantly between the two respiratory rates (p < 0.05) and between the three cuff pressures (p < 0.0001), and decreased significantly with decreasing cuff pressure (p < 0.05). The phase shift between Resp and modulation of OscP was statistically significant with respiratory rates (p < 0.05), but not with cuff pressures. It is accepted that BP in individuals is variable and that this relates to respiration; we now show that this respiration modulates oscillometric pulse and Korotkoff sound amplitudes from which BP is measured.

Characteristics of atrial fibrillation cycle length predict restoration of sinus rhythm by catheter ablation

BACKGROUND

Successful termination of atrial fibrillation (AF) during catheter ablation (CA) is associated with arrhythmia-free follow-up. Preablation factors such as mean atrial fibrillation cycle length (AFCL) predict the likelihood of AF termination during ablation but recurring patterns and AFCL stability have not been evaluated.

OBJECTIVE

To investigate novel predictors of acute and postoperative ablation outcomes from intracardiac electrograms: (1) recurring AFCL patterns and (2) localization index (LI) of the instantaneous fibrillatory rate distribution.

METHODS

Sixty-two patients with AF (32 paroxysmal AF; 45 men; age 57 ± 10 years) referred for CA were enrolled. One-minute electrogram was recorded from coronary sinus (CS; 5 bipoles) and right atrial appendage (HRA; 2 bipoles). Atrial activations were detected automatically to derive the AFCL and instantaneous fibrillatory rate (inverse of AFCL) time series. Recurring AFCL patterns were quantified by using recurrence plot indices (RPIs): percentage determinism, entropy of determinism, and maximum diagonal length. AFCL stability was determined by using the LI. The CA outcome predictivity of individual indices was assessed.

RESULTS

Patients with terminated atrial fibrillation (T-AF) had higher RPI (P < .05 in CS7-8) and LI than did those with nonterminated atrial fibrillation (P < .005 in CS3-4; P < .05 in CS5-6, CS7-8, and HRA). Patients free of arrhythmia after 3-month follow-up had higher RPI and LI (all P < .05 in CS7-8). All indices except percentage determinism predicted T-AF in CS7-8 (area under the curve [AUC] ≥ 0.71; odds ratio [OR] ≥ 4.50; P < .05). The median AFCL and LI predicted T-AF in HRAD (AUC ≥ 0.75; OR ≥ 7.76; P < .05). The RPI and LI predicted 3-month follow-up in CS7-8 (AUC ≥ 0.68; OR ≥ 4.17; P < .05).

CONCLUSIONS

AFCL recurrence and stability indices could be used in selecting patients more likely to benefit from CA.

Evaluation of an algorithm based on single-condition decision rules for binary classification of 12-lead ambulatory ECG recording quality

A new algorithm for classifying ECG recording quality based on the detection of commonly observed ECG contaminants which often render the ECG unusable for diagnostic purposes was evaluated. Contaminants (baseline drift, flat line, QRS-artefact, spurious spikes, amplitude stepwise changes, noise) were detected on individual leads from joint time-frequency analysis and QRS amplitude. Classification was based on cascaded single-condition decision rules (SCDR) that tested levels of contaminants against classification thresholds. A supervised learning classifier (SLC) was implemented for comparison. The SCDR and SLC algorithms were trained on an annotated database (Set A, PhysioNet Challenge 2011) of 'acceptable' versus 'unacceptable' quality recordings using the 'leave M out' approach with repeated random partitioning and cross-validation. Two training approaches were considered: (i) balanced, in which training records had equal numbers of 'acceptable' and 'unacceptable' recordings, (ii) unbalanced, in which the ratio of 'acceptable' to 'unacceptable' recordings from Set A was preserved. For each training approach, thresholds were calculated, and classification accuracy of the algorithm compared to other rule based algorithms and the SLC using a database for which classifications were unknown (Set B PhysioNet Challenge 2011). The SCDR algorithm achieved the highest accuracy (91.40%) compared to the SLC (90.40%) in spite of its simple logic. It also offers the advantage that it facilitates reporting of meaningful causes of poor signal quality to users.