The Bayesian approach for real-world implementation of plasma p-tau217 in tertiary care memory clinics in Thailand.

INTRODUCTION: Plasma phosphorylated tau (p-tau)217 is a promising biomarker for Alzheimer's disease (AD) diagnosis, but its clinical implementation remains challenging. We propose a strategy based on Bayes' theorem and test it in real-life memory clinics. METHODS: Memory clinic patients were evaluated by neurocognitive specialists for prespecified diagnosis and subsequently underwent blood collection for p-tau217, cerebrospinal fluid, or amyloid positron emission tomography. Using cross-validation, the Bayesian approach (pretest probability × individualized likelihood ratio) was compared to other models for AD diagnosis. RESULTS: The Bayesian strategy demonstrated an area under the receiver operating characteristic curve (AUC) of 0.98 (95% confidence interval [CI]: 0.96-1.0), significantly outperforming multivariable logistic regression (p-tau217, age, apolipoprotein E; AUC 0.95, p = 0.024) and p-tau217 alone (AUC = 0.94, p = 0.007). When applying the two-threshold approach, the Bayesian strategy yielded an accuracy of 0.94 (95% CI: 0.88-1.0) without requiring confirmatory tests in 62.9% of the iterations. DISCUSSION: The Bayesian strategy offers an effective and flexible approach to address the limitations of plasma p-tau217 in clinical practice. HIGHLIGHTS: Incorporating pretest probability into the interpretation of plasma phosphorylated tau (p-tau)217 improves the diagnostic performance significantly. The strategy could obviate the need for confirmatory testing in most of the patients. Plasma p-tau217 proves useful as a biomarker for Alzheimer's disease in low- and middle-income country such as Thailand.

Conflicting Sensory Information Sharpens the Neural Representations of Early Selective Visuospatial Attention.

Adaptive behaviors require the ability to resolve conflicting information caused by the processing of incompatible sensory inputs. Prominent theories of attention have posited that early selective attention helps mitigate cognitive interference caused by conflicting sensory information by facilitating the processing of task-relevant sensory inputs and filtering out behaviorally irrelevant information. Surprisingly, many recent studies that investigated the role of early selective attention on conflict mitigation have failed to provide positive evidence. Here, we examined changes in the selectivity of early visuospatial attention in male and female human subjects performing an attention-cueing Eriksen flanker task, where they discriminated the shape of a visual target surrounded by congruent or incongruent distractors. We used the inverted encoding model to reconstruct spatial representations of visual selective attention from the topographical patterns of amplitude modulations in alpha band oscillations in scalp EEG (∼8-12 Hz). We found that the fidelity of the alpha-based spatial reconstruction was significantly higher in the incongruent compared with the congruent condition. Importantly, these conflict-related modulations in the reconstruction fidelity occurred at a much earlier time window than those of the lateralized posterior event-related potentials associated with target selection and distractor suppression processes, as well as conflict-related modulations in the frontocentral negative-going wave and midline-frontal theta oscillations (∼3-7 Hz), thought to track executive control functions. Taken together, our data suggest that conflict resolution is supported by the cascade of neural processes underlying early selective visuospatial attention and frontal executive functions that unfold over time.

Sustained attention operates via dissociable neural mechanisms across different eccentric locations.

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

Sustained attention operates via dissociable neural mechanisms across different eccentric locations.

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

Attentive pairwise interaction network for AI-assisted clock drawing test assessment of early visuospatial deficits.

Dementia is a debilitating neurological condition which impairs the cognitive function and the ability to take care of oneself. The Clock Drawing Test (CDT) is widely used to detect dementia, but differentiating normal from borderline cases requires years of clinical experience. Misclassifying mild abnormal as normal will delay the chance to investigate for potential reversible causes or slow down the progression. To help address this issue, we propose an automatic CDT scoring system that adopts Attentive Pairwise Interaction Network (API-Net), a fine-grained deep learning model that is designed to distinguish visually similar images. Inspired by how humans often learn to recognize different objects by looking at two images side-by-side, API-Net is optimized using image pairs in a contrastive manner, as opposed to standard supervised learning, which optimizes a model using individual images. In this study, we extend API-Net to infer Shulman CDT scores from a dataset of 3108 subjects. We compare the performance of API-Net to that of convolutional neural networks: VGG16, ResNet-152, and DenseNet-121. The best API-Net achieves an F1-score of 0.79, which is a 3% absolute improvement over ResNet-152's F1-score of 0.76. The code for API-Net and the dataset used have been made available at https://github.com/cccnlab/CDT-API-Network .

A gradual transition from veridical to categorical representations along the visual hierarchy during working memory, but not perception.

The ability to stably maintain visual information over brief delays is central to cognitive functioning. One possible way to achieve robust working memory maintenance is by having multiple concurrent mnemonic representations across multiple cortical loci. For example, early visual cortex might contribute to storage by representing information in a "sensory-like" format, while intraparietal sulcus uses a format transformed away from sensory driven responses. As an explicit test of mnemonic code transformations along the visual hierarchy, we quantitatively modeled the progression of veridical-to-categorical orientation representations in human participants. Participants directly viewed, or held in mind, an oriented grating pattern, and the similarity between fMRI activation patterns for different orientations was calculated throughout retinotopic cortex. During direct perception, similarity was clustered around cardinal orientations, while during working memory the obliques were represented more similarly. We modeled these similarity patterns based on the known distribution of orientation information in the natural world: The "veridical" model uses an efficient coding framework to capture hypothesized representations during visual perception. The "categorical" model assumes that different "psychological distances" between orientations result in orientation categorization relative to cardinal axes. During direct perception, the veridical model explained the data well in early visual areas, while the categorical model did worse. During working memory, the veridical model only explained some of the data, while the categorical model gradually gained explanatory power for increasingly anterior retinotopic regions. These findings suggest that directly viewed images are represented veridically, but once visual information is no longer tethered to the sensory world, there is a gradual progression to more categorical mnemonic formats along the visual hierarchy.

Feature-based attention warps the perception of visual features.

Selective attention improves sensory processing of relevant information but can also impact the quality of perception. For example, attention increases visual discrimination performance and at the same time boosts apparent stimulus contrast of attended relative to unattended stimuli. Can attention also lead to perceptual distortions of visual representations? Optimal tuning accounts of attention suggest that processing is biased towards "off-tuned" features to maximize the signal-to-noise ratio in favor of the target, especially when targets and distractors are confusable. Here, we tested whether such tuning gives rise to phenomenological changes of visual features. We instructed participants to select a color among other colors in a visual search display and subsequently asked them to judge the appearance of the target color in a 2-alternative forced choice task. Participants consistently judged the target color to appear more dissimilar from the distractor color in feature space. Critically, the magnitude of these perceptual biases varied systematically with the similarity between target and distractor colors during search, indicating that attentional tuning quickly adapts to current task demands. In control experiments we rule out possible non-attentional explanations such as color contrast or memory effects. Overall, our results demonstrate that selective attention warps the representational geometry of color space, resulting in profound perceptual changes across large swaths of feature space. Broadly, these results indicate that efficient attentional selection can come at a perceptual cost by distorting our sensory experience.

Sural-sparing pattern: A study against electrodiagnostic subtypes of Guillain-Barre syndrome.

Objective: To study sural-sparing pattern in Guillain-Barre syndrome (GBS) and compare it among GBS's electrodiagnostic subtypes, classified by two recent criteria. Methods: This study retrospectively reviewed clinical data and electrodiagnostic studies (EDXs) of 88 GBS patients diagnosed in a tertiary care hospital (2010-2019). Results: Overall, 79/88 (89.8%) and 36/45 (80%) patients had bilateral sensory nerve conduction studies (NCS) in the first EDX and follow-up EDX, respectively. Sural-sparing occurred in all subtypes (50% overall occurrence rate), most commonly in demyelination. There was no statistically significant difference in sural-sparing occurrence rates between demyelinating and axonal GBS; however, sural-sparing in axonal GBS tended to show a lower number of abnormal upper-limb sensory nerve action potentials (SNAPs) than demyelinating GBS. Shifting between sural-sparing and no sural-sparing occurred in approximately-one-fourth of patients receiving serial studies. Follow-up EDX additionally discovered 20% of all sural-sparing. Unilateral EDX could have omitted up to 30% of sural-sparing. Conclusions: Sural-sparing is less obviously manifested in axonal than demyelinating GBS, with respect to the number of affected upper-limb SNAPs. Extended sensory NCS is worth in detecting sural-sparing as a supportive electrodiagnostic GBS feature. Significance: This report showed one different character of sural-sparing (number of affected upper-limb SNAPs) between demyelinating and axonal GBS.

Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments.

Mild cognitive impairment (MCI) is an early stage of cognitive decline or memory loss, commonly found among the elderly. A phonemic verbal fluency (PVF) task is a standard cognitive test that participants are asked to produce words starting with given letters, such as "F" in English and "ก" /k/ in Thai. With state-of-the-art machine learning techniques, features extracted from the PVF data have been widely used to detect MCI. The PVF features, including acoustic features, semantic features, and word grouping, have been studied in many languages but not Thai. However, applying the same PVF feature extraction methods used in English to Thai yields unpleasant results due to different language characteristics. This study performs analytical feature extraction on Thai PVF data to classify MCI patients. In particular, we propose novel approaches to extract features based on phonemic clustering (ability to cluster words by phonemes) and switching (ability to shift between clusters) for the Thai PVF data. The comparison results of the three classifiers revealed that the support vector machine performed the best with an area under the receiver operating characteristic curve (AUC) of 0.733 (N = 100). Furthermore, our implemented guidelines extracted efficient features, which support the machine learning models regarding MCI detection on Thai PVF data.

An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks.

BACKGROUND: Mild cognitive impairment (MCI) is an early stage of cognitive decline which could develop into dementia. An early detection of MCI is a crucial step for timely prevention and intervention. Recent studies have developed deep learning models to detect MCI and dementia using a bedside task like the classic clock drawing test (CDT). However, it remains a challenge to predict the early stage of the disease using the CDT data alone. Moreover, the state-of-the-art deep learning techniques still face black box challenges, making it questionable to implement them in a clinical setting. METHODS: We recruited 918 subjects from King Chulalongkorn Memorial Hospital (651 healthy subjects and 267 MCI patients). We propose a novel deep learning framework that incorporates data from the CDT, cube-copying, and trail-making tests. Soft label and self-attention were applied to improve the model performance and provide a visual explanation. The interpretability of the visualization of our model and the Grad-CAM approach were rated by experienced medical personnel and quantitatively evaluated using intersection over union (IoU) between the models' heat maps and the regions of interest. RESULTS: Rather than using a single CDT image in the baseline VGG16 model, using multiple drawing tasks as inputs into our proposed model with soft label significantly improves the classification performance between the healthy aging controls and the MCI patients. In particular, the classification accuracy increases from 0.75 (baseline model) to 0.81. The F1-score increases from 0.36 to 0.65, and the area under the receiver operating characteristic curve (AUC) increases from 0.74 to 0.84. Compared to the multi-input model that also offers interpretable visualization, i.e., Grad-CAM, our model receives higher interpretability scores given by experienced medical experts and higher IoUs. CONCLUSIONS: Our model achieves better classification performance at detecting MCI compared to the baseline model. In addition, the model provides visual explanations that are superior to those of the baseline model as quantitatively evaluated by experienced medical personnel. Thus, our work offers an interpretable machine learning model with high classification performance, both of which are crucial aspects of artificial intelligence in medical diagnosis.

An adaptive perspective on visual working memory distortions.

When holding multiple items in visual working memory, representations of individual items are often attracted to, or repelled from, each other. While this is empirically well-established, existing frameworks do not account for both types of distortions, which appear to be in opposition. Here, we demonstrate that both types of memory distortion may confer functional benefits under different circumstances. When there are many items to remember and subjects are near their capacity to accurately remember each item individually, memories for each item become more similar (attraction). However, when remembering smaller sets of highly similar but discernible items, memory for each item becomes more distinct (repulsion), possibly to support better discrimination. Importantly, this repulsion grows stronger with longer delays, suggesting that it dynamically evolves in memory and is not just a differentiation process that occurs during encoding. Furthermore, both attraction and repulsion occur even in tasks designed to mitigate response bias concerns, suggesting they are genuine changes in memory representations. Together, these results are in line with the theory that attraction biases act to stabilize memory signals by capitalizing on information about an entire group of items, whereas repulsion biases reflect a tradeoff between maintaining accurate but distinct representations. Both biases suggest that human memory systems may sacrifice veridical representations in favor of representations that better support specific behavioral goals. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA).

The Montreal cognitive assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning; its scores are based on the number of unique correct words produced by the test taker. However, it is possible that unique words may be counted differently for various languages. This study focuses on Thai as a language that differs from English in terms of word combinations. We applied various automatic speech recognition (ASR) techniques to develop an assisted scoring system for the MoCA language fluency test with Thai language support. This was a challenge because Thai is a low-resource language for which domain-specific data are not publicly available, especially speech data from patients with MCIs. Furthermore, the great variety of pronunciation, intonation, tone, and accent of the patients, all of which might differ from healthy controls, bring more complexity to the model. We propose a hybrid time delay neural network hidden Markov model (TDNN-HMM) architecture for acoustic model training to create our ASR system that is robust to environmental noise and to the variation of voice quality impacted by MCI. The LOTUS Thai speech corpus was incorporated into the training set to improve the model's generalization. A preprocessing algorithm was implemented to reduce the background noise and improve the overall data quality before feeding data into the TDNN-HMM system for automatic word detection and language fluency score calculation. The results show that the TDNN-HMM model in combination with data augmentation using lattice-free maximum mutual information (LF-MMI) objective function provides a word error rate (WER) of 30.77%. To our knowledge, this is the first study to develop an ASR with Thai language support to automate the scoring system of MoCA's language fluency assessment.

Founder effect of the TTTCA repeat insertions in SAMD12 causing BAFME1.

Benign adult familial myoclonic epilepsy type 1 (BAFME1) in several Japanese and Chinese families has recently been found to be caused by pentanucleotide repeat expansions in SAMD12. We identified a Thai family with six members affected with BAFME. Microsatellite studies suggested a linkage to the BAFME1 region on chromosome 8q24. Subsequently, long-read whole-genome sequencing showed the (TTTTA)446(TTTCA)149 in intron 4 of SAMD12 in an affected member. Repeat-primed PCR and long-range PCR revealed that the pentanucleotide repeat expansions segregated with the disease status. Our Thai family is the first non-Japanese and non-Chinese family with BAFME1. SNP array showed that the aberrant repeats had the same haplotype as those previously determined in Japanese and Chinese patients suggesting a common ancestry. The variant is estimated to arise ~12,000 years ago.

Associations between hypoxia parameters in obstructive sleep apnea and cognition, cortical thickness, and white matter integrity in middle-aged and older adults.

OBJECTIVE: This study aimed to investigate the association between each parameter of intermittent hypoxia in obstructive sleep apnea (OSA) and the cognitive profile, cortical thickness, and white matter integrity in middle-aged and older adults. METHODOLOGY: Participants were newly diagnosed with moderate or severe OSA from the King Chulalongkorn Memorial Hospital, Bangkok, Thailand. Respiratory parameters from polysomnography were extracted. Each participant was tested on a battery of neuropsychological tests and underwent an MRI scan of the brain. Cortical thickness analysis and diffusion tensor imaging analysis were performed. Participants were classified as having either severe or mild hypoxia based on parameters of hypoxia, i.e., oxygen desaturation index, lowest oxygen saturation, and the percentage of total sleep time spent below 90% oxygen saturation. RESULTS: Of 17 patients with OSA, there were 8 men (47%). Median age was 57 years and median AHI was 60.6. Comparison of cortical thickness between the severe and the mild group of each hypoxic parameter revealed two clusters of cortical thinning at the right inferior frontal gyrus (p-value = 0.008) and right inferior parietal gyrus (p-value = 0.006) in the severe desaturation group and a cluster of cortical thinning at the superior parietal gyrus (p-value = 0.008) in the high oxygen desaturation index group. There was no difference in cognitive function or white matter integrity between groups. CONCLUSIONS: The magnitude of the degree and frequency of desaturations in OSA are associated with a decrease in cortical thickness at the frontal and parietal regions.

Constructing calendars in the brain.

By studying an enigmatic condition called, "calendar synesthesia", we explored the elusive boundary between perception, visual imagery, and the manner in which we construct an internal mental calendar by mapping time-sequences onto spatial maps. We use a series of demonstrations to establish that these calendars act more like real objects activating sensory pathways rather than purely abstract symbolic descriptions that bear no resemblance to an actual calendar. We propose that the calendar is enshrined in acircuitry involving the hippocampal place-cells and entorhinal grid-cells, which are connected to the angular gyrus (involved with computing sequences) via the inferior longitudinal fasciculus.

TTTCA repeat insertions in an intron of YEATS2 in benign adult familial myoclonic epilepsy type 4.

Epilepsy is a common neurological disorder and identification of its causes is important for a better understanding of its pathogenesis. We previously studied a Thai family with a type of epilepsy, benign adult familial myoclonic epilepsy type 4 (BAFME4), and localized its gene to chromosome 3q26.32-q28. Here, we used single-molecule real-time sequencing and found expansions of TTTTA and insertions of TTTCA repeats in intron 1 of YEATS2 in one affected member of the family. Of all the available members in the family-comprising 13 affected and eight unaffected-repeat-primed PCR and long-range PCR revealed the co-segregation of the TTTCA repeat insertions with the TTTTA repeat expansions and the disease status. For 1116 Thai control subjects, none were found to harbour the TTTCA repeats while four had the TTTTA repeat expansions. Therefore, our findings suggest that BAFME4 is caused by the insertions of the intronic TTTCA repeats in YEATS2. Interestingly, all four types of BAFMEs for which underlying genes have been found (BAFMEs 1, 4, 6 and 7) are caused by the same molecular pathology, suggesting that the insertions of non-coding TTTCA repeats are involved in their pathogenesis.

Coexisting representations of sensory and mnemonic information in human visual cortex.

Traversing sensory environments requires keeping relevant information in mind while simultaneously processing new inputs. Visual information is kept in working memory via feature-selective responses in early visual cortex, but recent work has suggested that new sensory inputs obligatorily wipe out this information. Here we show region-wide multiplexing abilities in classic sensory areas, with population-level response patterns in early visual cortex representing the contents of working memory alongside new sensory inputs. In a second experiment, we show that when people get distracted, this leads to both disruptions of mnemonic information in early visual cortex and decrements in behavioral recall. Representations in the intraparietal sulcus reflect actively remembered information encoded in a transformed format, but not task-irrelevant sensory inputs. Together, these results suggest that early visual areas play a key role in supporting high-resolution working memory representations that can serve as a template for comparison with incoming sensory information.

Multivariate Analysis of BOLD Activation Patterns Recovers Graded Depth Representations in Human Visual and Parietal Cortex.

Navigating through natural environments requires localizing objects along three distinct spatial axes. Information about position along the horizontal and vertical axes is available from an object's position on the retina, while position along the depth axis must be inferred based on second-order cues such as the disparity between the images cast on the two retinae. Past work has revealed that object position in two-dimensional (2D) retinotopic space is robustly represented in visual cortex and can be robustly predicted using a multivariate encoding model, in which an explicit axis is modeled for each spatial dimension. However, no study to date has used an encoding model to estimate a representation of stimulus position in depth. Here, we recorded BOLD fMRI while human subjects viewed a stereoscopic random-dot sphere at various positions along the depth (z) and the horizontal (x) axes, and the stimuli were presented across a wider range of disparities (out to ∼40 arcmin) compared to previous neuroimaging studies. In addition to performing decoding analyses for comparison to previous work, we built encoding models for depth position and for horizontal position, allowing us to directly compare encoding between these dimensions. Our results validate this method of recovering depth representations from retinotopic cortex. Furthermore, we find convergent evidence that depth is encoded most strongly in dorsal area V3A.

Separating memoranda in depth increases visual working memory performance.

Visual working memory is the mechanism supporting the continued maintenance of information after sensory inputs are removed. Although the capacity of visual working memory is limited, memoranda that are spaced farther apart on a 2-D display are easier to remember, potentially because neural representations are more distinct within retinotopically organized areas of visual cortex during memory encoding, maintenance, or retrieval. The impact on memory of spatial separability in depth is less clear, even though depth information is essential to guiding interactions with objects in the environment. On one account, separating memoranda in depth may facilitate performance if interference between items is reduced. However, depth information must be inferred indirectly from the 2-D retinal image, and less is known about how visual cortex represents depth. Thus, an alternative possibility is that separation in depth does not attenuate between-items interference; it may even impair performance, as attention must be distributed across a larger volume of 3-D space. We tested these alternatives using a stereo display while participants remembered the colors of stimuli presented either near or far in the 2-D plane or in depth. Increasing separation in-plane and in depth both enhanced performance. Furthermore, participants who were better able to utilize stereo depth cues showed larger benefits when memoranda were separated in depth, particularly for large memory arrays. The observation that spatial separation in the inferred 3-D structure of the environment improves memory performance, as is the case in 2-D environments, suggests that separating memoranda in depth might reduce neural competition by utilizing cortically separable resources.

Relief from intractable phantom pain by combining psilocybin and mirror visual-feedback (MVF).

AL's leg was amputated resulting in phantom-limb pain (PLP). (1) When a volunteer placed her foot on or near the phantom - touching it evoked organized sensations in corresponding locations on AL's phantom. (2) Mirror-visual-feedback (MVF) relieved PLP, as did, "phantom massage". (3) Psilocybin-MVF pairing produced synergistic effects, complete elimination of PLP, and reduction in paroxysmal episodes. (4) Touching the volunteer's leg where AL previously had external fixators, evoked sensation of nails boring through the leg. Using a "telescoping" nail, we created the illusion of a nail being removed with corresponding pain relief. (5) Artificial flames produced warmth in the phantom.