Driving Ability in Alzheimer Disease Spectrum: Neural Basis, Assessment, and Potential Use of Optic Flow Event-Related Potentials

Mapping Activity and Functional Organisation of the Motor and Visual Pathways Using ADC-fMRI in the Human Brain

In contrast to blood-oxygenation level-dependent (BOLD) functional MRI (fMRI), which relies on changes in blood flow and oxygenation levels to infer brain activity, diffusion fMRI (DfMRI) investigates brain dynamics by monitoring alterations in the apparent diffusion coefficient (ADC) of water. These ADC changes may arise from fluctuations in neuronal morphology, providing a distinctive perspective on neural activity. The potential of ADC as an fMRI contrast (ADC-fMRI) lies in its capacity to reveal neural activity independently of neurovascular coupling, thus yielding complementary insights into brain function. To demonstrate the specificity and value of ADC-fMRI, both ADC- and BOLD-fMRI data were collected at 3 T in human subjects during visual stimulation and motor tasks. The first aim of this study was to identify an acquisition design for ADC that minimises BOLD contributions. By examining the timings in responses, we report that ADC 0/1 timeseries (acquired with b values of 0 and 1 ms/μm 2 $$ {\upmu \mathrm{m}}^2 $$ ) exhibit residual vascular contamination, while ADC 0.2/1 timeseries (with b values of 0.2 and 1 ms/μm 2 $$ {\upmu \mathrm{m}}^2 $$ ) show minimal BOLD influence and higher sensitivity to neuromorphological coupling. Second, a general linear model was employed to identify activation clusters for ADC 0.2/1 and BOLD, from which the average ADC and BOLD responses were calculated. The negative ADC response exhibited a significantly reduced delay relative to the task onset and offset as compared to BOLD. This early onset further supports the notion that ADC is sensitive to neuromorphological rather than neurovascular coupling. Remarkably, in the group-level analysis, positive BOLD activation clusters were detected in the visual and motor cortices, while the negative ADC clusters mainly highlighted pathways in white matter connected to the motor cortex. In the averaged individual level analysis, negative ADC activation clusters were also present in the visual cortex. This finding confirmed the reliability of negative ADC as an indicator of brain function, even in regions with lower vascularisation such as white matter. Finally, we established that ADC-fMRI time courses yield the expected functional organisation of the visual system, including both grey and white matter regions of interest. Functional connectivity matrices were used to perform hierarchical clustering of brain regions, where ADC-fMRI successfully reproduced the expected structure of the dorsal and ventral visual pathways. This organisation was not replicated with the b = 0.2 ms/μm 2 $$ {\upmu \mathrm{m}}^2 $$ diffusion-weighted time courses, which can be seen as a proxy for BOLD (via T2-weighting). These findings underscore the robustness of ADC time courses in functional MRI studies, offering complementary insights into BOLD-fMRI regarding brain function and connectivity patterns.

Magnetoencephalographic brain activity evoked by the optic-flow task is correlated with β-amyloid burden and parahippocampal atrophy

Visuospatial perception is often impaired in people with Alzheimer's disease (AD). Because visuospatial information is thought to be processed in the visual dorsal stream, it is believed that brain activities in the dorsal stream will be altered in AD patients. In this study, we investigated whether regional brain activity related to visuospatial perception were associated with AD progression markers. An optic-flow task, which activates the dorsal stream associated with visuospatial perception, was performed, and the brain activities evoked by the task were evaluated using magnetoencephalography (MEG). First, we evaluated the responses to optic-flow stimuli in 21 cognitively unimpaired participants and determined the regions of interest (ROIs) where optic-flow activities were activated. Task-related activations were observed in 14 cortical regions including the dorsal stream: the right and left medial ventral occipital cortex (MVOcC), lateral occipital cortex (LOcC), precuneus (Pcun), inferior parietal lobule (IPL), superior parietal lobule (SPL), posterior superior temporal sulcus (pSTS), and fusiform gyri (FuG). Next, we performed correlation analyses between task-related activity in each ROI and two AD progression markers, global amyloid burden and parahippocampal gyrus (PHG) volume, for 25 participants who underwent amyloid positron emission tomography (PET) scans. We found that the global amyloid burden was negatively correlated with task-related activity in the left MVOcC and right SPL [r = -0.488 (p = 0.013) and r = -0.421 (p = 0.038), respectively]. Furthermore, significant positive correlations were observed between PHG volume and task-related activity in both the left and right SPL [r = 0.500 (p = 0.011) and r = 0.549 (p = 0.005), respectively]. Since the SPL is known to be responsible for visuospatial perception, these results suggest that MEG neuronal activity of patients performing the optic-flow activity can detect changes in brain activity associated with visuospatial impairment related to AD.

Exploration of Hippocampal Functional Connectivity Alterations in Patients with High Myopia via Seed-Based Functional Connectivity Analysis

Aim

The objective of this study was to examine changes in functional connectivity (FC) in the hippocampus among patients with high myopia (HM) compared to healthy controls (HCs) through the utilization of seed-based functional connectivity (FC) analysis.

Methods

Resting-state functional magnetic resonance imaging (fMRI) was conducted on a sample of 82 patients diagnosed with high myopia (HM) and 59 HCs. The two groups were matched based on age, weight and other relevant variables. Using seed-based FC analysis to detect alterations in hippocampal FC patterns in HM patients and HCs. Furthermore, a correlation analysis was performed to examine the associations between the mean functional connectivity (FC) signals in various brain regions of patients with HM and their corresponding clinical manifestations.

Results

The FC values in the left temporal pole-temporal gyrus (L-TPOsup), right hippocampus (R-HIP), left medial temporal gyrus (L-MTG) and left hippocampus in HM patients were significantly lower than those of healthy subjects. In the left temporal pole-superior temporal gyrus (L-TPOsup), right orbital part of middle frontal gyrus (RO-MFG), left fusiform gyrus (L-FG), left cerebellum superior (L-Cbe6), left middle temporal gyrus (L-MTG), right thalamus (R-THA), and right hippocampus, FC values were also significantly lower.

Conclusion

Brain dysfunction was observed in various regions of the HM patients, suggesting the existence of neurobiological alterations that could lead to impairments in visual cognition, movement, emotional processing, and visual memory.

Bayesian workflow for the investigation of hierarchical classification models from tau-PET and structural MRI data across the Alzheimer's disease spectrum

Background

Alzheimer's disease (AD) diagnosis in its early stages remains difficult with current diagnostic approaches. Though tau neurofibrillary tangles (NFTs) generally follow the stereotypical pattern described by the Braak staging scheme, the network degeneration hypothesis (NDH) has suggested that NFTs spread selectively along functional networks of the brain. To evaluate this, we implemented a Bayesian workflow to develop hierarchical multinomial logistic regression models with increasing levels of complexity of the brain from tau-PET and structural MRI data to investigate whether it is beneficial to incorporate network-level information into an ROI-based predictive model for the presence/absence of AD.

Methods

This study included data from the Translational Biomarkers in Aging and Dementia (TRIAD) longitudinal cohort from McGill University's Research Centre for Studies in Aging (MCSA). Baseline and 1 year follow-up structural MRI and [18F]MK-6240 tau-PET scans were acquired for 72 cognitive normal (CN), 23 mild cognitive impairment (MCI), and 18 Alzheimer's disease dementia subjects. We constructed the four following hierarchical Bayesian models in order of increasing complexity: (Model 1) a complete-pooling model with observations, (Model 2) a partial-pooling model with observations clustered within ROIs, (Model 3) a partial-pooling model with observations clustered within functional networks, and (Model 4) a partial-pooling model with observations clustered within ROIs that are also clustered within functional brain networks. We then investigated which of the models had better predictive performance given tau-PET or structural MRI data as an input, in the form of a relative annualized rate of change.

Results

The Bayesian leave-one-out cross-validation (LOO-CV) estimate of the expected log pointwise predictive density (ELPD) results indicated that models 3 and 4 were substantially better than other models for both tau-PET and structural MRI inputs. For tau-PET data, model 3 was slightly better than 4 with an absolute difference in ELPD of 3.10 ± 1.30. For structural MRI data, model 4 was considerably better than other models with an absolute difference in ELPD of 29.83 ± 7.55 relative to model 3, the second-best model.

Conclusion

Our results suggest that representing the data generating process in terms of a hierarchical model that encompasses both ROI-level and network-level heterogeneity leads to better predictive ability for both tau-PET and structural MRI inputs over all other model iterations.

Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg

Background and purpose

To develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg.

Materials and methods

This retrospective study included 60 subjects [30 Alzheimer's disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference.

Results

The single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects.

Conclusion

Our deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.

Driving Assessment for Persons with Dementia: How and when?

Dementia is a progressive neurodegenerative disease leading to deterioration in cognitive and physical skills. Driving is an important instrumental activity of daily living, essential for independence. However, this is a complex skill. A moving vehicle can be a dangerous tool in the hand of someone who cannot maneuver it properly. As a result, the assessment of driving capacity should be part of the management of dementia. Moreover, dementia comprises of different etiologies and stages consisting of different presentations. As a result, this study aims to identify driving behaviors common in dementia and compare different assessment methods. A literature search was conducted using the PRISMA checklist as a framework. A total of forty-four observational studies and four meta-analyses were identified. Study characteristics varied greatly with regards to methodology, population, assessments, and outcome measures used. Drivers with dementia performed generally worse than cognitively normal drivers. Poor speed maintenance, lane maintenance, difficulty managing intersections and poor response to traffic stimuli were the most common behaviors in drivers with dementia. Naturalistic driving, standardized road assessments, neuropsychological tests, participant self-rating and caregiver rating were the most common driving assessment methods used. Naturalistic driving and on-road assessments had the highest predictive accuracy. Results on other forms of assessments varied greatly. Both driving behaviors and assessments were influenced by different stages and etiologies of dementia at varying degrees. Methodology and results in available research are varied and inconsistent. As a result, better quality research is required in this field.

Exploration of static functional connectivity and dynamic functional connectivity alterations in the primary visual cortex among patients with high myopia via seed-based functional connectivity analysis

Aim

This study was conducted to explore differences in static functional connectivity (sFC) and dynamic functional connectivity (dFC) alteration patterns in the primary visual area (V1) among high myopia (HM) patients and healthy controls (HCs) via seed-based functional connectivity (FC) analysis.

Methods

Resting-state functional magnetic resonance imaging (fMRI) scans were performed on 82 HM patients and 59 HCs who were closely matched for age, sex, and weight. Seed-based FC analysis was performed to identify alterations in the sFC and dFC patterns of the V1 in HM patients and HCs. Associations between mean sFC and dFC signal values and clinical symptoms in distinct brain areas among HM patients were identified via correlation analysis. Static and dynamic changes in brain activity in HM patients were investigated by assessments of sFC and dFC via calculation of the total time series mean and sliding-window analysis.

Results

In the left anterior cingulate gyrus (L-ACG)/left superior parietal gyrus (L-SPG) and left V1, sFC values were significantly greater in HM patients than in HCs. In the L-ACG and right V1, sFC values were also significantly greater in HM patients than in HCs [two-tailed, voxel-level P < 0.01, Gaussian random field (GRF) correction, cluster-level P < 0.05]. In the left calcarine cortex (L-CAL) and left V1, dFC values were significantly lower in HM patients than in HCs. In the right lingual gyrus (R-LING) and right V1, dFC values were also significantly lower in HM patients than in HCs (two-tailed, voxel-level P < 0.01, GRF correction, cluster-level P < 0.05).

Conclusion

Patients with HM exhibited significantly disturbed FC between the V1 and various brain regions, including L-ACG, L-SPG, L-CAL, and R-LING. This disturbance suggests that patients with HM could exhibit impaired cognitive and emotional processing functions, top-down control of visual attention, and visual information processing functions. HM patients and HCs could be distinguished from each other with high accuracy using sFC and dFC variabilities. These findings may help to identify the neural mechanism of decreased visual performance in HM patients.

Driving Ability Evaluation and Rehabilitation for People With Alzheimer's Disease and Related Dementias

Worldwide, it is estimated that around 50 million older adults have Alzheimer's disease and related dementias (ADRD). Cognitive deficits associated with ADRD may affect a driver's perception and decision-making and potentially cause safety concerns. Despite much research, there lacks a comprehensive cognitive evaluation to determine the driving capability of a person with ADRD and it is unclear what are the most effective training and interventions that help to enhance driving performance for these individuals. The purpose of this article is to conduct a comprehensive literature survey to review and summarize studies of driving performance evaluation and intervention for people with ADRD and discuss perspectives for future studies. Although many studies have investigated the correlations between driving behaviors and cognitive performances for people with ADRD, it remains unclear how driving behaviors and cognitive performances are associated with psychophysiological measures. We discussed the need to develop regular driving evaluation and rehabilitation protocol for people with ADRD. We also highlighted the potential benefit to combine driving tests with psychophysiological measures to assist in characterizing personalized cognitive evaluation in the behavioral evaluation process.

A visual object segmentation algorithm with spatial and temporal coherence inspired by the architecture of the visual cortex

Scene analysis in video sequences is a complex task for a computer vision system. Several schemes have been addressed in this analysis, such as deep learning networks or traditional image processing methods. However, these methods require thorough training or manual adjustment of parameters to achieve accurate results. Therefore, it is necessary to develop novel methods to analyze the scenario information in video sequences. For this reason, this paper proposes a method for object segmentation in video sequences inspired by the structural layers of the visual cortex. The method is called Neuro-Inspired Object Segmentation, SegNI. SegNI has a hierarchical architecture that analyzes object features such as edges, color, and motion to generate regions that represent the objects in the scenario. The results obtained with the Video Segmentation Benchmark VSB100 dataset demonstrate that SegNI can adapt automatically to videos with scenarios that have different nature, composition, and different types of objects. Also, SegNI adapts its processing to new scenario conditions without training, which is a significant advantage over deep learning networks.

Mandarin version of dementia and driving decision aid (DDDA): Development and stakeholder evaluation in Taiwan

BACKGROUND

Dementia causes cognitive and memory difficulties which can reduce the driving safety of the individuals. The decision-making process for driving retirement is challenging, and yet limited guidance is available.

OBJECTIVES

This article reports the development of the Taiwanese version of dementia and driving decision aid (DDDA) and the evaluation from stakeholders through a dementia and driving education programme.

METHODS

A multi-method approach was adopted using a pre-test, post-test survey and focus group interviews. A total of 154 healthcare professionals, family caregivers and people with dementia participated education programme, and 12 experts attended the focus group discussion. The survey included demographics, knowledge, confidence, competence and awareness of using DDDA. Participants completed a survey prior and immediately after the education programme. We translated a 32-page interactive DDDA booklet from the original English version to Mandarin. The education programme consisted of three-hour dementia and driving education module delivered both face-to-face and online.

RESULTS

The majority of participants described the booklet as balanced (91.7%) with the information presented in a 'good' or 'excellent' manner (93.4%). Most participants (85.3%) felt that DDDA helps them in making decisions about driving. Five themes were extracted from the focus group interview: (1) approach targeted to people with dementia, (2) specific content and additional information, (3) culturally appropriate modification, (4) having the right to drive and (5) booklet dissemination. The knowledge, confidence, competence and awareness of using the DDDA increased significantly (p < 0.001) after the education programmes.

CONCLUSION

We anticipate that use of the DDDA booklet will raise awareness of this social and health issue among the general public and facilitate collaborations with clinicians, municipalities and related organisations in providing a decision-making resource material for those with people living with dementia and their families. This study was not a clinical trial and the focus of this study was development and evaluation of the DDDA booklet. As mentioned in the methods section, participants were invited to attend the education program and provided their thoughts on the DDDA booklet based on their satisfaction level. Moreover, the education program was a one-day, workshop type program. This study was neither "prospectively assigns human participants or groups of humans to one or more health-related interventions" nor "to evaluate the effects on health outcomes", according to the definition of clinical trial by WHO. Therefore, we did not consider this study was a clinical trial.

IMPLICATIONS FOR PRACTICE

There is an urgent need for supporting people with dementia and their families to negotiate the complex decision-making involved in deciding to change their approach to driving. The DDDA booklet can fill an important gap in service delivery to people with dementia who are adjusting to life without driving.

Decreased stimulus-driven connectivity of the primary visual cortex during visual motion stimulation in amnestic mild cognitive impairment: An fMRI study

Motion perceptual deficits are common in Alzheimer's disease (AD). Although the posterior parietal cortex is thought to play a critical role in these deficits, it is currently unclear whether the primary visual cortex (V1) contributes to these deficits in AD. To elucidate this issue, we investigated the net activity or connectivity within V1 in 17 amnestic mild cognitive impairment (aMCI) patients, 17 AD patients and 17 normal controls (NC) using functional magnetic resonance imaging (fMRI). fMRI was recorded under two conditions: visual motion stimulation and resting-state. The net activity or connectivity within V1 extracted by independent component analysis (ICA) was significantly increased during visual motion stimuli compared with that of the resting-state condition in NC, but not in aMCI or AD patients. These findings suggest the alteration of the net activity or connectivity within V1, which may contribute to the previously reported motion perceptual deficits in aMCI and AD. Therefore, the decreased net V1 activity measured as the strength of the ICA component may provide a new disease biomarker for early detection of AD.