Is it time to change the way we detect Alzheimer’s disease and monitor its progression? Towards affordable and theory-driven approaches from cognitive neurosciences

A large proportion of people suffering from Alzheimer’s disease (AD) worldwide are not receiving a timely diagnosis. The tools currently used to detect AD and monitor its progression are not sensitive to the preclinical stages and lack specificity for correctdiagnosis. Available biomarkers show acceptable levels of sensitivity but remain littlespecific and not accessible to everyone. We embrace the view that enhancing cognitive assessment of AD should be a research priority. This Perspective paper focuses on issues which, to our view, have been preventing cognitive tests from meeting outstanding needs in the early of detection, monitoring, and treatment development of AD dementia.We first outline the limitations of current diagnostic procedures both theoretically and practically. We then provide a rationale for theory-driven cognitive approaches which would allow mapping assessment tools to specific neuropathological stages of the neurodegenerative course of AD. Finally, we propose research strategies that would help test a hypothesis which, though launched five years ago, remains untested.That is: “Which memory system is impaired first in Alzheimer’s disease?

Healthy aging and visual working memory:The effect of mixing feature and conjunction changes

It has been suggested that an age-related decrease in the ability to bind and retain conjunctions of features may account for some of the pronounced decline of visual working memory across the adult life-span. So far the evidence for this proposal has been mixed with some suggesting a specific deficit in binding to location, while the retention of surface feature conjunctions (e.g. color-shape) appears to remain largely intact. The present experiments follow up on the results of an earlier study, which found that older adults were specifically poor at detecting conjunction changes when they were mixed with trials containing changes to individual features, relative to when these trials were blocked (Cowan et al., 2006, Dev. Psychol., 42, pp. 1089). Using stimuli defined by conjunctions of color and shape (Experiment 1), and color and location (Experiment 2) we find no evidence that older adults are less accurate at detecting binding changes when trial types are mixed. Further, analysis of estimates of discriminability provides substantial-to-strong evidence against this suggestion. We discuss these findings in relation to previous studies addressing the same question and suggest that much of the evidence for specific age-related VWM binding deficits is not as strong as it first appears

Complex tensor factorisation with PARAFAC2 for the estimation of brain connectivity from the EEG

Objective: The coupling between neuronal populations and its magnitude have been shown to be informative for various clinical applications. One method to estimate functional brain connectivity is with electroencephalography (EEG) from which the cross-spectrum between different sensor locations is derived. We wish to test the efficacy of tensor factorisation in the estimation of brain connectivity. Methods: An EEG model in the complex domain is derived that shows the suitability of the PARAFAC2 model. Complex tensor factorisation based on PARAFAC2 is used to decompose the EEG into scalp components described by the spatial, spectral, and complex trial profiles. A connectivity metric is also derived on the complex trial profiles of the extracted components. Results: Results on a benchmark EEG dataset confirmed that PARAFAC2 can estimate connectivity better than traditional tensor analysis such as PARAFAC within a range of signal-tonoise ratios. MVAR-ICA outperformed PARAFAC2 for very low signal-to-noise ratios while being inferior in most of the range, and in contrast to our method MVAR-ICA does not allow the estimation of trial to trial information. The analysis of EEG from patients with mild cognitive impairment or Alzheimer’s disease showed that PARAFAC2 identifies loss of brain connectivity agreeing with prior pathological knowledge. Conclusion: The complex PARAFAC2 algorithm is suitable for EEG connectivity estimation since it allows to extract meaningful coupled sources and provides better estimates than complex PARAFAC and MVAR-ICA. Significance: A new paradigm that employs complex tensor factorisation has demonstrated to be successful in identifying brain connectivity and the location of couples sources for both a benchmark and a real-world EEG dataset. This can enable future applications and has the potential to solve some the issues that deteriorate the performance of traditional connectivity metrics

Drivers of information needs : a behavioural study – exploring searcher's feeling-of-knowing

The searcher's realisation of Information Need (IN) in Information Retrieval (IR) is triggered by a perception of the knowledge gap the searcher perceives. Introspective epistemic (knowledge) feelings are evoked, describing the state of the user's anomaly. For instance, Feeling-of-Knowing (FOK) refers to a state of a user's temporary unavailability to recall the information in question. The role and the extent to which such epistemic feelings inform the user's cognitive context need further research. Our methodological design followed the Recall-Judgment-Recognition (RJR) paradigm, commonly used as a framework for memory tests. We collected behavioural data from twenty-four participants in a general knowledge Q/A user study to investigate the interplay of users' internal perceptions of knowing based on three metacognitive states (Recall). The results showed significant differences across different metacognitive states and subsequent memory retrieval performance (Recognition), leading to our conclusion of the accuracy of the metacognitive states of knowing. Specifically, we found that FOK was only a relatively accurate predictor of MR. The amount of failures of recognition connected to FOK, thus, suggests that the participants might have misattributed their positive FOK. Participants could not recognise the answer as they thought, giving rise to phenomena such as Illusion of Knowing. Furthermore, our data support the significant effect of task (question) difficulty on participants' metacognitive states. Based on the interactions between Recall and Recognition, our results contribute to the understanding of the graded nature of cognitive functions, supporting the user's cognitive context in information search and expanding such an area to the realm of contextual task difficulty

Understanding feeling-of-knowing in information search : an EEG study

The realisation and the variability of information needs (IN) with respect to a searcher’s gap in knowledge is driven by the perceived Anomalous State of Knowledge (ASK). The concept of Feeling-of-Knowing (FOK), as the introspective feeling of knowledge awareness, shares the characteristics of an ASK state. From an IR perspective, FOK as a premise to trigger IN is unexplored. Motivated by the neuroimaging studies in IR, we investigate the neurophysiological drivers associated with FOK, to provide evidence validating FOK as a distinctive state in IN realisation. We employ Electroencephalography to capture the brain activity of 24 healthy participants performing a textual Question Answering IR scenario. We analyse the evoked neural patterns corresponding to three states of knowledge: i.e., (1)“I know”, (2)“FOK”, (3)“I do not know”. Our findings show the distinct neurophysiological signatures (N1, P2, N400, P6) in response to information segments processed in the context of our three levels. They further reveal that the brain manifestation associated with “FOK” does not significantly differ from the ones associated with “I do not know”, indicating their association with recognition of a gap in knowledge and as such could further inform the IN formation on different levels of knowing

Graph-Based Permutation Patterns for the Analysis of Task-Related fMRI Signals on DTI Networks in Mild Cognitive Impairment

Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for univariate time series. Very recently, Permutation Entropy for Graph signals ($PE_G$) has been proposed to extend $PE$ to data residing on irregular domains. However, $PE_G$ is limited as it provides a single value to characterise a whole graph signal. Here, we introduce a novel approach to evaluate graph signals at the vertex level: graph-based permutation patterns. Synthetic datasets show the efficacy of our method. We reveal that dynamics in graph signals, undetectable with $PE_G$, can be discerned using our graph-based permutation patterns. These are then validated in the analysis of DTI and fMRI data acquired during a working memory task in mild cognitive impairment, where we explore functional brain signals on structural white matter networks. Our findings suggest that graph-based permutation patterns change in individual brain regions as the disease progresses. Thus, graph-based permutation patterns offer promise by enabling the granular scale analysis of graph signals.Comment: 5 pages, 5 figures, 1 tabl

Promising developments in neuropsychological approaches for the detection of preclinical Alzheimer’s disease: a selective review

Recently published guidelines suggest that the most opportune time to treat individuals with Alzheimer’s disease is during the preclinical phase of the disease. This is a phase when individuals are defined as clinically normal but exhibit evidence of amyloidosis, neurodegeneration and subtle cognitive/behavioral decline. While our standard cognitive tests are useful for detecting cognitive decline at the stage of mild cognitive impairment, they were not designed for detecting the subtle cognitive variations associated with this biomarker stage of preclinical Alzheimer’s disease. However, neuropsychologists are attempting to meet this challenge by designing newer cognitive measures and questionnaires derived from translational efforts in neuroimaging, cognitive neuroscience and clinical/experimental neuropsychology. This review is a selective summary of several novel, potentially promising, approaches that are being explored for detecting early cognitive evidence of preclinical Alzheimer’s disease in presymptomatic individuals

Visual processing during short-term memory binding in mild Alzheimer's disease

Patients with Alzheimer's disease (AD) typically present with attentional and oculomotor abnormalities that can have an impact on visual processing and associated cognitive functions. Over the last few years, we have witnessed a shift toward the analyses of eye movement behaviors as a means to further our understanding of the pathophysiology of common disorders such as AD. However, little work has been done to unveil the link between eye moment abnormalities and poor performance on cognitive tasks known to be markers for AD patients, such as the short-term memory-binding task. We analyzed eye movement fixation behaviors of thirteen healthy older adults (Controls) and thirteen patients with probable mild AD while they performed the visual short-term memory binding task. The short-term memory binding task asks participants to detect changes across two consecutive arrays of two bicolored object whose features (i.e., colors) have to be remembered separately (i.e., Unbound Colors), or combined within integrated objects (i.e., Bound Colors). Patients with mild AD showed the well-known pattern of selective memory binding impairments. This was accompanied by significant impairments in their eye movements only when they processed Bound Colors. Patients with mild AD remarkably decreased their mean gaze duration during the encoding of color-color bindings. These findings open new windows of research into the pathophysiological mechanisms of memory deficits in AD patients and the link between its phenotypic expressions (i.e., oculomotor and cognitive disorders). We discuss these findings considering current trends regarding clinical assessment, neural correlates, and potential avenues for robust biomarkers

Role of executive functions in the conversion from mild cognitive impairment to dementia

BACKGROUND: Recent research pointed to executive dysfunction as a potential early predictor of the progression of mild cognitive impairment (MCI) to dementia in Alzheimer's clinical syndrome (ACS). Such cognitive impairments account for functional impairments in instrumental activities of daily living (IADL). OBJECTIVE: The present study analyzes the contributions of executive functions to predict MCI-dementia progression in ACS. METHODS: We assessed 145 participants, 51 cognitively unimpaired and 94 MCI. The latter were divided using the traditional, memory-based MCI classification (single domain amnestic, multidomain amnestic, and non-amnestic). Eight tests assessing executive functions were administered at baseline and at 1-year follow-up, together with cognitive screening tools and IADL measures. MCI patients were reclassified based on the outcomes from a K-mean cluster analysis which identified three groups. A simple lineal regression model was used to examine whether the classification based on executive functioning could more accurately predict progression to dementia a year later. RESULTS: Clusters based on executive function deficits explained a significant proportion of the variance linked to MCI-dementia conversion, even after controlling for the severity of MCI at baseline (F(1, 68) = 116.25, p = 0.000, R2 = 0.63). Classical memory-based MCI classification failed to predict such a conversion (F(1, 68) = 5.09, p = 0.955, R2 = 0.07). Switching, categories generation, and planning were the executive functions that best distinguished between MCI converters and stable. CONCLUSION: MCI with a dysexecutive phenotype significantly predicts conversion to dementia in ACS a year later. Switching abilities and verbal fluency (categories) must be evaluated in MCI patients to assess risk of future dementia