Brain imaging in dementia

Catatonia in patients with dementia: A descriptive study of clinical profiles and treatment response

BACKGROUND

Catatonia is a highly prevalent syndrome in patients presenting with major neurocognitive disorders (dementia). In this study, we aim to provide a comprehensive description of the clinical and therapeutic aspects of catatonia in patients with dementia.

METHOD

This descriptive study, conducted between September 2015 and June 2022, collected data from 25 patients diagnosed with dementia, out of 143 patients treated for catatonia in our specialized psychiatry department. We collected sociodemographic, clinical and treatment data for each patient.

RESULTS

Dementia patients constituted 17% of the catatonic cases. Predominantly female, the cohort had a mean age of 65. Diagnoses included Alzheimer's (4 patients, 17%) and Parkinson's (1 patient, 4%) diseases, Lewy body dementia (5 patients, 21%), vascular dementia (4 patients, 17%) and frontotemporal lobar degeneration (10 patients, 41%). The mean Bush-Francis Catatonia Rating Scale score upon admission was 20/69. Overall, complete remission of catatonia was achieved in 75% of patients (n=18), with only 13% (n=3) responding to lorazepam alone, while others required additional interventions such as electroconvulsive therapy (ECT) and/or amantadine. Vascular dementia was predominantly observed in cases resistant to treatment.

CONCLUSION

The findings indicate a frequent co-occurrence of catatonia and dementia, highlighting treatability yet suggesting a potential for resistance to lorazepam, which varies by dementia diagnosis. Investigating the mechanisms underlying this resistance and the variability in treatment response is crucial for developing more precise therapeutic strategies.

Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture

BACKGROUND

Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking.

AIMS

This research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall.

METHODS & PROCEDURES

This is a cross-sectional, online, self-administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence-Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi-QA-MPNet (Multi-Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence-Transformer.

OUTCOMES & RESULTS

The results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task.

CONCLUSIONS & IMPLICATIONS

This finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images.

WHAT THIS PAPER ADDS

What is already known on this subject It is already known that speech- and language-based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech- and language-based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations. What this paper adds to existing knowledge This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care. What are the potential or actual clinical implications of this work? The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life.

The use of neuroimaging techniques in the early and differential diagnosis of dementia

Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer's disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.

Analysis of emotional prosody as a tool for differential diagnosis of cognitive impairments: a pilot research

Introduction

This pilot research was designed to investigate if prosodic features from running spontaneous speech could differentiate dementia of the Alzheimer's type (DAT), vascular dementia (VaD), mild cognitive impairment (MCI), and healthy cognition. The study included acoustic measurements of prosodic features (Study 1) and listeners' perception of emotional prosody differences (Study 2).

Methods

For Study 1, prerecorded speech samples describing the Cookie Theft picture from 10 individuals with DAT, 5 with VaD, 9 with MCI, and 10 neurologically healthy controls (NHC) were obtained from the DementiaBank. The descriptive narratives by each participant were separated into utterances. These utterances were measured on 22 acoustic features via the Praat software and analyzed statistically using the principal component analysis (PCA), regression, and Mahalanobis distance measures.

Results

The analyses on acoustic data revealed a set of five factors and four salient features (i.e., pitch, amplitude, rate, and syllable) that discriminate the four groups. For Study 2, a group of 28 listeners served as judges of emotions expressed by the speakers. After a set of training and practice sessions, they were instructed to indicate the emotions they heard. Regression measures were used to analyze the perceptual data. The perceptual data indicated that the factor underlying pitch measures had the greatest strength for the listeners to separate the groups.

Discussion

The present pilot work showed that using acoustic measures of prosodic features may be a functional method for differentiating among DAT, VaD, MCI, and NHC. Future studies with data collected under a controlled environment using better stimuli are warranted.

Functional near-infrared spectroscopy in elderly patients with four types of dementia

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) is commonly used to study human brain function by measuring the hemodynamic signals originating from cortical activation and provides a new noninvasive detection method for identifying dementia.

AIM

To investigate the fNIRS imaging technique and its clinical application in differential diagnosis of subtype dementias including frontotemporal lobe dementia, Lewy body dementia, Parkinson’s disease dementia (PDD) and Alzheimer’s disease (AD).

METHODS

Four patients with different types of dementia were examined with fNIRS during two tasks and a resting state. We adopted the verbal fluency task, working memory task and resting state task. Each patient was compared on the same task. We conducted and analyzed the fNIRS data using a general linear model and Pearson’s correlation analysis.

RESULTS

Compared with other types of dementias, fNIRS showed the left frontotemporal and prefrontal lobes to be poorly activated during the verbal fluency task in frontotemporal dementia. In Lewy body dementia, severe asymmetry of prefrontal lobes appeared during both verbal fluency and working memory tasks, and the patient had low functional connectivity during a resting state. In PDD, the patient’s prefrontal cortex showed lower excitability than the temporal lobe during the verbal fluency task, while the prefrontal cortex showed higher excitability during the working memory task. The patient with AD showed poor prefrontal and temporal activation during the working memory task, and more activation of frontopolar instead of the dorsolateral prefrontal cortex.

CONCLUSION

Different hemodynamic characteristics of four types of dementia (as seen by fNIRS imaging) provides evidence that fNIRS can serve as a potential tool for the diagnosis between dementia subtypes.

[Correction of Partial Volume Effect Using CT Images in Brain 18F-FDG PET]

PURPOSE

We performed partial volume effect correction of PET images using ¹⁸F-FDG-PET and CT images taken consecutively, compared it with correction using MRI images, and investigated the usefulness of correction using CT images.

METHODS

A total of 9 clinically normal subjects were included in the study, and the CT and MRI images of each subject were segmented and normalized. PET images were coregistered to each morphological image and then normalized. The normalized morphological images of each subject were used to mask the brain atlas and to correct for the partial volume effect. For each brain region, comparison of counts, two-group test between CT- and MRI-corrected groups, and correlation analysis were performed.

RESULTS

As a result of correction, some error was observed between the two groups. Correlation analysis showed strong positive correlations in many areas, but weak correlations were found in some areas. In the region where significant differences were found, the two groups showed strong positive correlation, and in the region where weak correlation was found, the error tended to be small.

CONCLUSION

It is suggested that the correction by CT can be performed with the same accuracy, although some errors are generated compared with MRI.

Caring for patients with cognitive dysfunction, fluctuations and dementia caused by Parkinson's disease

Cognitive dysfunction is one of the most prevalent non-motor symptoms in patients with Parkinson's disease (PD). While it tends to worsen in the later stages of disease, it can occur at any time, with 15-20% of patients exhibiting cognitive deficits at diagnosis (Aarsland et al., 2010; Goldman and Sieg, 2020). The characteristic features of cognitive dysfunction include impairment in executive function, visuospatial abilities, and attention, which vary in severity from subtle impairment to overt dementia (Martinez-Horta and Kulisevsky, 2019). To complicate matters, cognitive dysfunction is prone to fluctuate in PD patients, impacting diagnosis and the ability to assess progression and decision-making capacity. The diagnosis of cognitive impairment or dementia has a huge impact on patient independence, quality of life, life expectancy and caregiver burden (Corallo et al., 2017; Lawson et al., 2016; Leroi et al., 2012). It is therefore essential that physicians caring for patients with PD provide education, screening and treatment for this aspect of the disease. In this chapter, we provide a practical guide for the assessment and management of various degrees of cognitive dysfunction in patients with PD by approaching the disease at different stages. We address risk factors for cognitive dysfunction, prevention strategies prior to making the diagnosis, available tools for screening. Lastly, we review aspects of care, management and considerations, including decision-making capacity, that occur after the patient has been diagnosed with cognitive dysfunction or dementia.

Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks

Background: In recent years, predicting and modeling the progression of Alzheimer's disease (AD) based on neuropsychological tests has become increasingly appealing in AD research. Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data. Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer's disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers. Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD. Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

Update on Major Neurocognitive Disorders

Dementia is a syndrome characterized by a gradually progressive course that spans a continuum from preclinical symptoms to major impairment in two or more cognitive domains with functional decline. In this review, the author examines some of the more common dementia syndromes from among dozens of different diseases. Findings show that as the U.S. population continues to age, the number of Americans with dementia is expected to rise drastically over the next several decades. This upsurge will contribute to increased health care costs and will have a significant public health impact. Neurodegenerative disorders such as Alzheimer's disease, frontotemporal degeneration, and alpha-synucleinopathies (e.g., Lewy body disease and Parkinson's disease) are some of the more prevalent causes for dementia. In recent years, advancements in neuroimaging, understanding of genetic contributions and pathological changes, and the development of novel biomarkers have fueled clinical understanding of these disorders. However, substantial disease-modifying therapies are still lagging. The advent of future interventions hinges on the ability to discern the distinct clinico-pathologic profiles of the various dementia syndromes and to identify reliable biomarkers for utilization in clinical trials.

Clinical use of neuro-imaging in psychiatric patients at the Charlotte Maxeke Johannesburg Academic Hospital

Background

Neuro-imaging is relatively new in psychiatry. Although the actual role of neuro-imaging in psychiatry remains unclear, it is used to strengthen clinical evidence in making psychiatric diagnoses.

Aim

To analyse the records of inpatients referred for neuro-imaging (computerised tomography [CT] and/or magnetic resonance imaging [MRI] scans) to determine the proportion of abnormal neuro-imaging results and, if any, factors associated with abnormal neuro-imaging results.

Setting

This study was conducted at the Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) situated in Johannesburg, South Africa.

Methods

This was a quantitative retrospective record review. All adult psychiatric inpatients who had undergone a CT and/or MRI scan during 01 January 2014 to 31 December 2015 were included. Out-patients or patients admitted in the medical wards were excluded from the study. All neuro-imaging referrals were identified from hospital records and their demographics, scan characteristics and diagnoses were subsequently captured.

Results

A total of 1040 patients were admitted to the CMJAH psychiatric unit, of which 213 (20.5%) underwent neuro-imaging tests. Of the 213 scans performed, 74 were abnormal, representing a yield of 34.7%. The most common reported pathology was atrophy (n = 22, 29.7%). There was no statistically significant association between age group (χ² = 3.9, p = 0.8), gender (χ² = 1.3; p = 0.5), psychiatric diagnoses and abnormal scans. However, there were trends towards an association with comorbid HIV infection (χ² = 3.476, p = 0.062) and comorbid substance abuse (χ² = 2.286, p = 0.091).

Conclusion

This study supports the need for clear clinical indications to justify the cost-effective use of neuro-imaging in psychiatry. This study’s high yield of abnormal CT scans, although similar to other studies, advocates that HIV positive testing and the presence of focal neurological signs will improve the yield further.

Regional Quantitative Magnetic Resonance Imaging Data Improve Screening Accuracy of Subjective Memory Complaints and Informant Reports of Cognitive Decline

OBJECTIVE

We investigated whether the addition of Alzheimer's disease-signature region cortical thickness (AD-Ct) and hippocampal volume (Hv) obtained from brain MRI to subjective memory complaints and informant-reports of cognitive decline enhances the screening accuracy for cognitive disorders in a memory clinic setting.

METHODS

120 participants (40 cognitively normal, 40 MCI, 40 dementia) underwent clinical evaluation, neuropsychological assessment, and brain MRI. The Subjective Memory Complaints Questionnaire (SMCQ) and Seoul Informant-Report Questionnaire for Dementia (SIRQD) were applied to assess subjective memory complaints and informant-reports of cognitive decline respectively. Logistic regression and ROC curve analyses were conducted to compare the screening abilities of SMCQ+SIRQD, SMCQ+SIRQD+Hv, and SMCQ+SIRQD+AD-Ct models for cognitive disorders.

RESULTS

SMCQ+SIRQD+Hv model indicated better screening accuracy for MCI and overall cognitive disorder (CDall) than SMCQ+ SIRQD model. SMCQ+SIRQD+AD-Ct model had superior screening accuracy for dementia in comparison to SMCQ+SIRQD model. ROC curve analyses revealed that SMCQ+SIRQD+Hv model had the greatest area under the curve (AUC) for screening MCI and CDall (AUC: 0.941 and 0.957), while SMCQ+SIRQD+AD-Ct model had the greatest AUC for screening dementia (AUC: 0.966).

CONCLUSION

Our results suggest that the addition of regional quantitative MRI data enhances the screening ability of subjective memory complaints and informant-reports of cognitive decline for MCI and dementia.

ACR Appropriateness Criteria® Dementia

Degenerative disease of the central nervous system is a growing public health concern. The primary role of neuroimaging in the workup of patients with probable or possible Alzheimer disease has typically been to exclude other significant intracranial abnormalities. In general, the imaging findings in structural studies, such as MRI, are nonspecific and have limited potential in differentiating different types of dementia. Advanced imaging methods are not routinely used in community or general practices for the diagnosis or differentiation of forms of dementia. Nonetheless, in patients who have been evaluated by a dementia expert, FDG-PET helps to distinguish Alzheimer disease from frontotemporal dementia. In patients with suspected dementia with Lewy bodies, functional imaging of the dopamine transporter (ioflupane) using SPECT may be helpful. In patients with suspected normal-pressure hydrocephalus, DTPA cisternography and HMPAO SPECT/CT brain may provide assessment. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Validation of an Magnetic Resonance Imaging Acquisition and Review Protocol for Alzheimer's Disease and Related Disorders

Purpose

Magnetic resonance imaging (MRI) of the brain allows for the identification of structural lesions typical of Alzheimer's disease (AD), the main cause of dementia. However, to have a clinical impact, it is imperative that acquisition and reporting of this MRI-based evidence be standardized, ensuring the highest possible reliability and reproducibility. Our objective was to validate a systematic radiological MRI acquisition and review process in the context of AD.

Methods

We included 100 individuals with a suspicion of dementia due to AD for whom MRI were acquired using our proposed protocol of clinically achievable acquisitions and used a unified reading grid to gather semi-quantitative evidence guiding diagnostic. MRIs were read by 3 raters with different experience levels. Interrater reliability was measured using Cohen's kappa statistic.

Results

Interrater reliability average for lesions occupying space, hemorrhage, or ischemia, was respectively 0.754, 0.715, and 0.501. Average reliability of white matter hyperintensity burden (Fazekas), global cortical atrophy, and temporal lobe atrophy (Scheltens) scales was 0.687, 0.473, and 0.621 (right)/0.599 (left), respectively. The kappas for regional cortical atrophy (frontal, parietal, occipital, temporal, and posterior cingulum) varied from 0.281–0.678. The average MRI reading time varied between 1.43-5.22 minutes.

Conclusions

The presence of space occupying lesions, hemorrhagic or ischemic phenomena, and radiological scales have a good interrater reproducibility in MRI. Coupled with standardized acquisitions, such a protocol should be used when evaluating possible dementias, especially those due to probable AD.

Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing

Background

Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes. Multi-centre studies increase generalisability and statistical power. However, there is a lack of practical guidelines for the set-up and conduct of large neuroimaging studies.

Methods

We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss.

Results

The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data.

Conclusions

Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage.

Developing a core outcome set for people living with dementia at home in their neighbourhoods and communities: study protocol for use in the evaluation of non-pharmacological community-based health and social care interventions

BACKGROUND

The key aim of the study is to establish an agreed standardised core outcome set (COS) for use when evaluating non-pharmacological health and social care interventions for people living at home with dementia.

METHODS/DESIGN

Drawing on the guidance and approaches of the Core Outcome Measures in Effectiveness Trials (COMET), this study uses a four-phase mixed-methods design: 1 Focus groups and interviews with key stakeholder groups (people living with dementia, care partners, relevant health and social care professionals, researchers and policymakers) and a review of the literature will be undertaken to build a long list of outcomes. 2 Two rounds of Delphi surveys will be used with key stakeholder groups. Statements for the Delphi surveys and participation processes will be developed and informed through substantial member involvement with people living with dementia and care partners. A consensus meeting will be convened with key participant groups to discuss the key findings and finalise the COS. 3 A systematic literature review will be undertaken to assess the properties of tools and instruments to assess components of the COS. Measurement properties, validity and reliability will be assessed using the Consensus-based Standards for the Selection of Health Measurement (COSMIN) and COMET guidance. 4 A stated preference survey will elicit the preferences of key stakeholders for the outcomes identified as important to measure in the COS.

DISCUSSION

To the best of our knowledge, this study is the first to use a modified Delphi process to involve people living with dementia as a participant group. Though the study is confined to collecting data in the United Kingdom, use of the COS by researchers will enhance the comparability of studies evaluating non-pharmacological and community-based interventions.

TRIAL REGISTRATION

The study is registered on the COMET initiative, registered in 2014 at comet-initiative.org .

Diagnostic imaging in the management of patients with metabolic syndrome

Metabolic syndrome (MetS) is the constellation of metabolic risk factors that might foster development of type 2 diabetes and cardiovascular disease. Abdominal obesity and insulin resistance play a prominent role among all metabolic traits of MetS. Because intervention including weight loss can reduce these morbidity and mortality in MetS, early detection of the severity and complications of MetS could be useful. Recent advances in imaging modalities have provided significant insight into the development and progression of abdominal obesity and insulin resistance, as well as target organ injuries. The purpose of this review is to summarize advances in diagnostic imaging modalities in MetS that can be applied for evaluating each components and target organs. This may help in early detection, monitoring target organ injury, and in turn developing novel therapeutic target to alleviate and avert them.

New horizons in multimodality molecular imaging and novel radiotracers

Positron emission tomography (PET)/computerised tomography is now established in clinical practice for oncologic and non-oncological applications. Improvement and development of scanner hardware has allowed faster acquisitions and wider application. PET/magnetic resonance imaging offers potential improvements in diagnostic accuracy and patient acceptability but clinical applications are still being developed. A range of new radiotracers and non-radioactive contrast agents is likely to lead to a growth in hybrid molecular imaging applications that will allow better characterisation of disease processes.