Hypnosis, hypnotic suggestibility, and meditation : an integrative review of the associated brain regions and networks

The number of neuroimaging studies on hypnosis and meditation has multiplied rapidly in recent years. The methods and analytic techniques that are being applied are becoming increasingly sophisticated and approaches focusing on connectomics have offered novel ways to investigate the practices, enabling brain function to be investigated like never before. This chapter provides a review of the literature on the effects of hypnosis and meditation on brain network functional connectivity. Numerous cross-sectional as well as longitudinal studies have also reported enduring transformations in brain structure and function in practitioners of meditation, while evidence is mounting which demonstrates a relationship between hypnotic suggestibility and variations in neuroanatomy/functional connectivity that may facilitate hypnosis. The similarities (and differences) between the brain regions and networks associated with each type of practice are highlighted, while links are tentatively made between these and the reported phenomenology

Revealing the differences between normal and pathological ageing using functional magnetic resonance imaging (fMRI)

The aim of the present study was to use fMRI to examine the brain activation patterns found in normal and pathological ageing on specific cognitive tasks. The cognitive paradigms that were chosen, consisted of an n-back working memory task and a semantic memory and processing task. Manipulation of the n-back task enabled vigilance and working memory load to be investigated. Patients with Alzheimer's Disease (AD) and individuals with amnestic Mild Cognitive Impairment (MCI) were compared to normal elderly and young controls. The experiments showed that the patterns of brain activation found in normal and pathological ageing do not appear to fall along the same continuum. When comparing the elderly group to the young group, areas of under-activation could be seen, in addition to other regions of activation which were thought to be due to compensation. The comparison of the normal to the pathological groups revealed distinct differences in the levels and locations of the significant activations. On the vigilance and working memory tasks, the behavioural scores and reaction times of the pathological groups were not significantly different from the normal elderly, yet substantial differences could be identified in the brain activation patterns. The semantic memory task, contrary to expectation, revealed a significant difference in behavioural performance between the young group and the elderly group. Both the reaction times and the performance scores of the AD group were significantly different compared to the elderly, however. Significant differences also occurred in the brain activation patterns of both pathological groups (AD and MCI) compared to the elderly. The differences that were present between the normal and pathological groups on each of the tasks, suggest that sensitive cognitive fMRI paradigms might be very useful in resolving diagnostic ambiguity in people at increased risk of developing AD

Prolonged cholinergic enrichment influences regional cortical activation in early Alzheimer's disease

Neuroimaging studies of cholinesterase inhibitor (ChEI) treatment in Alzheimer's disease (AD) indicate that the short and long term actions of ChEIs are dissimilar. fMRI studies of the ChEI rivastigmine have focused on its short term action. In this exploratory study the effect of prolonged (20 weeks) rivastigmine treatment on regional brain activity was measured with fMRI in patients with mild AD. Eleven patients with probable AD and nine age-matched controls were assessed with a Pyramids and Palm Trees semantic association and an n-back working memory fMRI paradigm. In the patient group only, the assessment was repeated after 20 weeks of treatment. There was an increase in task-related brain activity after treatment with activations more like those of normal healthy elderly. Behaviorally, however, there were no significant differences between baseline and retest scores, with a range of performance probably reflecting variation in drug efficacy across patients. Variable patient response and drug dynamic/kinetic factors in small patient groups will inevitably bias (either way) the effect size of any relevant drug related changes in activation. Future studies should take drug response into account to provide more insight into the benefits of ChEI drugs at the individual level

Eigenvector alignment : assessing functional network changes in amnestic mild cognitive impairment and Alzheimer's disease

Eigenvector alignment, introduced herein to investigate human brain functional networks, is adapted from methods developed to detect influential nodes and communities in networked systems. It is used to identify differences in the brain networks of subjects with Alzheimer’s disease (AD), amnestic Mild Cognitive Impairment (aMCI) and healthy controls (HC). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all functional connectivity changes before assessing the strength of the functional relationship, i.e. alignment, between any two regions. This is achieved by comparing the placement of regions in a Euclidean space defined by the network's dominant eigenvectors. Eigenvector alignment recognises the strength of bilateral connectivity in cortical areas of healthy control subjects, but also reveals degradation of this commissural system in those with AD. Surprisingly little structural change is detected for key regions in the Default Mode Network, despite significant declines in the functional connectivity of these regions. In contrast, regions in the auditory cortex display significant alignment changes that begin in aMCI and are the most prominent structural changes for those with AD. Alignment differences between aMCI and AD subjects are detected, including notable changes to the hippocampal regions. These findings suggest eigenvector alignment can play a complementary role, alongside established network analytic approaches, to capture how the brain's functional networks develop and adapt when challenged by disease processes such as AD

Moderating effects of self-perceived knowledge in a relevance assessment task : an EEG study

Relevance assessment, a crucial Human-computer Information Retrieval (HCIR) aspect, denotes how well retrieved information meets the user’s information need (IN). Recently, user-centred research benefited from the employment of brain imaging, which contributed to our understanding of relevance assessment and associated cognitive processes. However, the effect of contextual aspects, such as the searcher’s self-perceived knowledge (SPK) on relevance assessment and its underlying neurocognitive processes, has not been studied. This work investigates the impact of users’ SPK about a topic (i.e. ‘knowledgeable’ vs. ‘not knowledgeable’) on relevance assessments (i.e. ‘relevant’ vs. ‘non-relevant’). To do so, using electroencephalography (EEG), we measured the neural activity of twenty-five participants while they provided relevance assessments during the Question and Answering (Q/A) Task. In the analysis, we considered the effects of SPK and specifically how it modulates the brain activity underpinning relevance judgements. Data-driven analysis revealed significant event-related potential differences (P300/CPP, N400, LPC), which were modulated by searchers’ SPK in the context of relevance assessment. We speculate that SPK affects distinct cognitive processes associated with attention, semantic integration and categorisation, memory, and decision formation that underpin relevance assessment formation. Our findings are an important step toward a better understanding of the role users’ SPK plays during relevance assessment

Neuroanatomical and neuropsychological correlates of resting-state EEG diagnostic features in patients with Alzheimer's disease

Background: In the search for accurate, low cost biomarkers for Alzheimer’s disease (AD) and other dementias, quantitative electroencephalography (EEG) may offer a solution. In a recent multisite study by Cognision, patients with AD were assessed using the Alzheimer Disease Neuroimaging Initiative protocol, plus EEG assessment. The primary objective of the current analysis, was to examine the relationships between a resting-state (rs)EEG feature set (that best discriminated AD patients from controls) and neuroanatomical measures. The second objective was to identify the rsEEG measures that reflected disease staging. Method: Eighty-nine patients with mild AD (MMSE 21-26) were evaluated using a comprehensive neuropsychological assessment battery, 5 minute eyes-open rsEEG, and structural MRI. Correlations (Spearman’s) were assessed between the 35 rsEEG features (that most accurately discriminated the AD patients), neuroanatomical measures (derived using Freesurfer), and neuropsychological test results. Result: Cortical Thickness (CT) measures within the left posterior cingulate and right precuneus were related to alpha features. Beta features were associated with regions including the right entorhinal cortex, middle temporal, supramarginal, lingual, and paracentral cortex, in addition to the anterior cingulate cortex (ACC) and precuneus, bilaterally. Gamma features correlated with regions that included the right ACC and fronto-parietal cortex. Delta features were linked to the left fronto-parietal and right entorhinal cortex. Theta features were associated with the left ACC and visual cortex. In relation to disease staging – Clinical Dementia Rating scores were correlated with gamma features at frontal electrode sites, and with power over frequency bands, delta to beta, at Fz. Alpha features were associated with hippocampal volume (bilaterally), whereas some delta and theta features were linked to left hippocampal volume. Conclusion: These preliminary correlation analyses highlight multiple brain regions that appear to underpin the rsEEG abnormalities that occur due to AD. Given the rich data offered by both rsEEG and by structural MRI, future studies could investigate the combined potential for these techniques to classify the dementias

Globalising strategies to meet global challenges : the case of ageing and dementia

Dementia has been declared a Global Challenge [1]. However, strategies to tackle it are far from global. Epidemiological forecasts are more alarming for low and middle-income countries (LMIC) than for high-income countries (HIC), and yet provisions to support the former are scarce and, in some cases, as we discuss below, impractical. New initiatives are emerging to close these gaps. The Latin America and Caribbean Consortium on Dementia (LAC-CD) [2] and the Global Dementia Prevention Program (GloDePP Consortium; Wang, H. from Peking University and Chan, K.Y. from University of Edinburgh. Preventing dementia and improving dementia care: setting and addressing research priorities in China. Supported by Global Challenges Research Fund Networking Grants). They are seeking strategies to meet and map local and global challenges. Both consortia agree that actions to improve diagnosis and post-diagnostic support are of utmost priority. Here we discuss theory-driven, culturally valid, and interdisciplinary approaches that can yield affordable, reliable, and practical solutions to meet these outstanding needs

Multimodal prediction of Alzheimer's disease severity level based on resting-state EEG and structural MRI

While several biomarkers have been developed for the detection of Alzheimer's disease (AD), not many are available for the prediction of disease severity, particularly for patients in the mild stages of AD. In this paper, we explore the multimodal prediction of Mini-Mental State Examination (MMSE) scores using resting-state electroencephalography (EEG) and structural magnetic resonance imaging (MRI) scans. Analyses were carried out on a dataset comprised of EEG and MRI data collected from 89 patients diagnosed with minimal-mild AD. Three feature selection algorithms were assessed alongside four machine learning algorithms. Results showed that while MRI features alone outperformed EEG features, when both modalities were combined, improved results were achieved. The top-selected EEG features conveyed information about amplitude modulation rate-of-change, whereas top-MRI features comprised information about cortical area and white matter volume. Overall, a root mean square error between predicted MMSE values and true MMSE scores of 1.682 was achieved with a multimodal system and a random forest regression model

Functional alignments in brain connectivity networks

Alzheimer’s disease (AD) is a brain disconnection syndrome, where functional connectivity analysis can detect changes in neural activity in pre-dementia stages [8]. Functional connectivity networks from functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) are susceptible to signal noise from biologic artefacts (e.g. cardiac artefacts) and environmental sources (e.g. electrical interference). A particular challenge for EEG is volume conduction, whereby a signal from a single source propagates through biological tissue to be detected simultaneously by multiple sensors (channels). The imaginary part of coherency (iCOH) provides a measure for connectivity that avoids this signal contamination, by ignoring correlation between signals with zero or π-phase lag. This removes false instantaneous activity with connectivity denoting synchronised signals at a given time lag, but it does come at the cost of erasing true instantaneous activity. We propose eigenvector alignment (EA) as a method for evaluating pairwise relationships from network eigenvectors; revealing noise robust, structural, insights from functional connectivity networks

Rapid detection of heart failure using a spectroscopic liquid biopsy

Heart disease is growing annually across the globe with numbers expected to rise to 46% of the population by 2030. Early detection is vital for several reasons, firstly it improves the long-term prognosis of the patient by admitting them through the appropriate pathway faster, secondly it reduces healthcare costs by streamlining diagnosis and finally, in combination with management or treatment, it can prevent the progression of the disease which in turn improves the patient’s quality of life. Therefore, there lies an increasing need to develop assays which can rapidly detect heart disease at an early stage. The Dxcover® liquid biopsy platform employs infrared spectroscopy and artificial intelligence, to quickly analyse minute amounts of patient serum. In this study, discrimination between healthy controls and diseased patients was obtained with an area under the receiver operating characteristic curve (AUC) of 0.89. When assessing the heart failure vs all patients, which is most akin to what would be observed in a triage setting, the model when tuned to a minimum of 45% specificity yielded a sensitivity of 89% and an NPV of 0.996, conversely when sensitivity was set at a 45% minimum, the specificity was 96%, giving an NPV of 0.991 when using a 1.5% prevalence. Other models were assessed in parallel, but the performance of the ORFPLS model was overall superior to the other models tested. In this large scale (n = 404) proof-of-concept study, we have shown that the Dxcover liquid biopsy platform has the potential to be a viable triage tool in emergency and routine situations for the diagnosis of heart failure