Cognitive impairment in heart failure

The clinical syndrome of heart failure is one of the leading causes of hospitalisation and mortality in older adults. Due to ageing of the general population and improved survival from cardiac disease the prevalence of heart failure is rising. Despite the fact that the majority of patients with heart failure are aged over 65 years old, many with multiple co-morbidities, the association between cognitive impairment and heart failure has received relatively little research interest compared to other aspects of cardiac disease. The presence of concomitant cognitive impairment has implications for the management of patients with heart failure in the community. There are many evidence based pharmacological therapies used in heart failure management which obviously rely on patient education regarding compliance. Also central to the treatment of heart failure is patient self-monitoring for signs indicative of clinical deterioration which may prompt them to seek medical assistance or initiate a therapeutic intervention e.g. taking additional diuretic. Adherence and self-management may be jeopardised by cognitive impairment. Formal diagnosis of cognitive impairment requires evidence of abnormalities on neuropsychological testing (typically a result ≥1.5 standard deviation below the age-standardised mean) in at least one cognitive domain. Cognitive impairment is associated with an increased risk of dementia and people with mild cognitive impairment develop dementia at a rate of 10-15% per year, compared with a rate of 1-2% per year in healthy controls.1 Cognitive impairment has been reported in a variety of cardiovascular disorders. It is well documented among patients with hypertension, atrial fibrillation and coronary artery disease, especially after coronary artery bypass grafting. This background is relevant to the study of patients with heart failure as many, if not most, have a history of one or more of these co-morbidities. A systematic review of the literature to date has shown a wide variation in the reported prevalence of cognitive impairment in heart failure. This range in variation probably reflects small study sample sizes, differences in the heart failure populations studied (inpatients versus outpatients), neuropsychological tests employed and threshold values used to define cognitive impairment. The main aim of this study was to identify the prevalence of cognitive impairment in a representative sample of heart failure patients and to examine whether this association was due to heart failure per se rather than the common cardiovascular co-morbidities that often accompany it such as atherosclerosis and atrial fibrillation. Of the 817 potential participants screened, 344 were included in this study. The study cohort included 196 patients with HF, 61 patients with ischaemic heart disease and no HF and 87 healthy control participants. The HF cohort consisted of 70 patients with HF and coronary artery disease in sinus rhythm, 51 patients with no coronary artery disease in sinus rhythm and 75 patients with HF and atrial fibrillation. All patients with HF had evidence of HF-REF with a LVEF <45% on transthoracic echocardiography. The majority of the cohort was male and elderly. HF patients with AF were more likely to have multiple co-morbidities. Patients recruited from cardiac rehabilitation clinics had proven coronary artery disease, no clinical HF and a LVEF >55%. The ischaemic heart disease group were relatively well matched to healthy controls who had no previous diagnosis of any chronic illness, prescribed no regular medication and also had a LVEF >55%. All participants underwent the same baseline investigations and there were no obvious differences in baseline demographics between each of the cohorts. All 344 participants attended for 2 study visits. Baseline investigations including physiological measurements, electrocardiography, echocardiography and laboratory testing were all completed at the initial screening visit. Participants were then invited to attend their second study visit within 10 days of the screening visit. 342 participants completed all neuropsychological assessments (2 participants failed to complete 1 questionnaire). A full comprehensive battery of neuropsychological assessment tools were administered in the 90 minute study visit. These included three global cognitive screening assessment tools (mini mental state examination, Montreal cognitive assessment tool and the repeatable battery for the assessment of neuropsychological status) and additional measures of executive function (an area we believe has been understudied to date). In total there were 9 cognitive tests performed. These were generally well tolerated. Data were also collected using quality of life questionnaires and health status measures. In addition to this, carers of the study participant were asked to complete a measure of caregiver strain and an informant questionnaire on cognitive decline. The prevalence of cognitive impairment varied significantly depending on the neuropsychological assessment tool used and cut-off value used to define cognitive impairment. Despite this, all assessment tools showed the same pattern of results with those patients with heart failure and atrial fibrillation having poorer cognitive performance than those with heart failure in sinus rhythm. Cognitive impairment was also more common in patients with cardiac disease (either coronary artery disease or heart failure) than age-, sex- and education-matched healthy controls, even after adjustment for common vascular risk factors.

The influence of actions on auditory perception: cognitive and neural mechanisms

It is well known that self-generated stimuli are processed differently from externally generated stimuli. For example, many people have noticed since childhood that it is very difficult to make a self-tickling. In the auditory domain, self-generated sounds elicit smaller brain responses as compared to externally generated sounds, known as the sensory attenuation (SA) effect. SA is manifested in reduced amplitudes of evoked responses as measured through MEEG, decreased firing rates of neurons and a lower level of perceived loudness for self-generated sounds. The predominant explanation for SA is based on the idea that self-generated stimuli are predicted (e.g., the forward model account). It is the nature of their predictability that is crucial for SA. On the contrary, the sensory gating account emphasizes a general suppressive effect of actions on sensory processing, regardless of the predictability of the stimuli. Both accounts have received empirical support, which suggests that both mechanisms may exist. In chapter 2, three behavioural studies concerning the influence of motor activation on auditory perception were presented. Study 1 compared the effect of SA and attention in an auditory detection task and showed that SA was present even when substantial attention was paid to unpredictable stimuli. Study 2 compared the loudness perception of tones generated by others between Chinese and British participants. Compared to externally generated tones, a decrease in perceived loudness for others generated tones was found among Chinese but not among the British. In study 3, partial evidence was found that even when reading words that are related to action, auditory detection performance was impaired. In chapter 3, the classic SA effect of M100 suppression was replicated with MEG in study 4. With time-frequency analysis, a potential neural information processing sequence was found in auditory cortex. Prior to the onset of self-generated tones, there was an increase of oscillatory power in the alpha band. After the stimulus onset, reduced gamma power and alpha/beta phase locking were found. The three temporally segregated oscillatory events correlated with each other and with SA effect, which may be the underlying neural implementation of SA. In chapter 4, a TMS-MEG study was presented investigating the role of the cerebellum in adapting to delayed presentation of self-generated tones (study 5). It demonstrated that in sham stimulation condition, the brain can adapt to the delay (about 100 ms) within 300 trials of learning by showing a significant increase of SA effect in the suppression of M100, but not M200 component. Whereas after stimulating the cerebellum with a suppressive TMS protocol, the adaptation in M100 suppression disappeared and the pattern of M200 suppression reversed to M200 enhancement. These data support the idea that the suppressive effect of actions on auditory processing is a consequence of both motor driven sensory predictions and general sensory gating. The results also demonstrate the importance of neural oscillations in implementing SA effect and the critical role of the cerebellum in learning sensory predictions under sensory perturbation.