Are Bipolar Disorder and Schizophrenia Neuroanatomically Distinct? An Anatomical Likelihood Meta-analysis

Objective: There is renewed debate on whether modern diagnostic classification should adopt a dichotomous or dimensional approach to schizophrenia and bipolar disorder. This study synthesizes data from voxel-based studies of schizophrenia and bipolar disorder to estimate the extent to which these conditions have a common neuroanatomical phenotype. Methods: A post-hoc meta-analytic estimation of the extent to which bipolar disorder, schizophrenia, or both conditions contribute to brain gray matter differences compared to controls was achieved using a novel application of the conventional anatomical likelihood estimation (ALE) method. 19 schizophrenia studies (651 patients and 693 controls) were matched as closely as possible to 19 bipolar studies (540 patients and 745 controls). Result: Substantial overlaps in the regions affected by schizophrenia and bipolar disorder included regions in prefrontal cortex, thalamus, left caudate, left medial temporal lobe, and right insula. Bipolar disorder and schizophrenia jointly contributed to clusters in the right hemisphere, but schizophrenia was almost exclusively associated with additional gray matter deficits (left insula and amygdala) in the left hemisphere. Limitation: The current meta-analytic method has a number of constraints. Importantly, only studies identifying differences between controls and patient groups could be included in this analysis. Conclusion: Bipolar disorder shares many of the same brain regions as schizophrenia. However, relative to neurotypical controls, lower gray matter volume in schizophrenia is more extensive and includes the amygdala. This fresh application of ALE accommodates multiple studies in a relatively unbiased comparison. Common biological mechanisms may explain the neuroanatomical overlap between these major disorders, but explaining why brain differences are more extensive in schizophrenia remains challenging

Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD

Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors

The prevalence of short video platforms has spawned a lot of fake news videos, which have stronger propagation ability than textual fake news. Thus, automatically detecting fake news videos has been an important countermeasure in practice. Previous works commonly verify each news video individually with multimodal information. Nevertheless, news videos from different perspectives regarding the same event are commonly posted together, which contain complementary or contradictory information and thus can be used to evaluate each other mutually. To this end, we introduce a new and practical paradigm, i.e., cross-sample fake news video detection, and propose a novel framework, Neighbor-Enhanced fakE news video Detection (NEED), which integrates the neighborhood relationship of new videos belonging to the same event. NEED can be readily combined with existing single-sample detectors and further enhance their performances with the proposed graph aggregation (GA) and debunking rectification (DR) modules. Specifically, given the feature representations obtained from single-sample detectors, GA aggregates the neighborhood information with the dynamic graph to enrich the features of independent samples. After that, DR explicitly leverages the relationship between debunking videos and fake news videos to refute the candidate videos via textual and visual consistency. Extensive experiments on the public benchmark demonstrate that NEED greatly improves the performance of both single-modal (up to 8.34% in accuracy) and multimodal (up to 4.97% in accuracy) base detectors. Codes are available in https://github.com/ICTMCG/NEED.Comment: To appear in ACL 2023 Finding

Online social participation among older malaysian living with dementia during the pandemic COVID-19: the ‘take the purple pledge’ project

The ‘Take the Purple Pledge’ project a collaborative project between UKM and the Alzheimer’s Disease Foundation Malaysia (ADFM) was conducted. The undergraduate students from the Speech Sciences, Physiotherapy, Occupational Therapy, and Audiology programs organized this event as part of the Community Based Rehabilitation course requirement. The program’s main objective was to engage with individuals with dementia and their caregivers. The ‘Take the Purple Pledge’ project consisted of two parts: (i) an interactive session with individuals with dementia and (ii) posting posters and materials on social media to spread awareness about dementia among the public. The first part of the program was conducted via Zoom on 14th August 2021 from 8:00 am to 11:30 am, while the second part of the program was completed on the same date. For the interactive session, 99 participants comprising students, ADFM staff, individuals living with dementia, and their caregivers attended the Zoom session, filled with virtual activities such as exercises, memory games, gamebooks, checklists, and tours. Individuals with dementia, their caregivers and staff from ADFM provided positive feedback about the program. The ‘Take the Purple Pledge’ program not only allowed individuals with dementia and their caregivers to interact with other people, it also provided an opportunity for them to participate and be included in a social event during this challenging time

Autistic Disorders and Schizophrenia: Related or Remote? An Anatomical Likelihood Estimation

Shared genetic and environmental risk factors have been identified for autistic spectrum disorders (ASD) and schizophrenia. Social interaction, communication, emotion processing, sensorimotor gating and executive function are disrupted in both, stimulating debate about whether these are related conditions. Brain imaging studies constitute an informative and expanding resource to determine whether brain structural phenotype of these disorders is distinct or overlapping. We aimed to synthesize existing datasets characterizing ASD and schizophrenia within a common framework, to quantify their structural similarities. In a novel modification of Anatomical Likelihood Estimation (ALE), 313 foci were extracted from 25 voxel-based studies comprising 660 participants (308 ASD, 352 first-episode schizophrenia) and 801 controls. The results revealed that, compared to controls, lower grey matter volumes within limbic-striato-thalamic circuitry were common to ASD and schizophrenia. Unique features of each disorder included lower grey matter volume in amygdala, caudate, frontal and medial gyrus for schizophrenia and putamen for autism. Thus, in terms of brain volumetrics, ASD and schizophrenia have a clear degree of overlap that may reflect shared etiological mechanisms. However, the distinctive neuroanatomy also mapped in each condition raises the question about how this is arrived in the context of common etiological pressures

Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models

Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to condition frozen pre-training models. Though effective, it is particularly problematic in the few-shot scenario, where prompt tuning performance is sensitive to the initialization and requires a time-consuming process to find a good initialization, thus restricting the fast adaptation ability of the pre-training models. In addition, prompt tuning could undermine the generalizability of the pre-training models, because the learnable prompt tokens are easy to overfit to the limited training samples. To address these issues, we introduce a novel Gradient-RegulAted Meta-prompt learning (GRAM) framework that jointly meta-learns an efficient soft prompt initialization for better adaptation and a lightweight gradient regulating function for strong cross-domain generalizability in a meta-learning paradigm using only the unlabeled image-text pre-training data. Rather than designing a specific prompt tuning method, our GRAM can be easily incorporated into various prompt tuning methods in a model-agnostic way, and comprehensive experiments show that GRAM brings about consistent improvement for them in several settings (i.e., few-shot learning, cross-domain generalization, cross-dataset generalization, etc.) over 11 datasets. Further, experiments show that GRAM enables the orthogonal methods of textual and visual prompt tuning to work in a mutually-enhanced way, offering better generalizability beyond the uni-modal prompt tuning methods.Comment: Accepted by ICCV 202

Frontal-Subcortical Protein Expression following Prenatal Exposure to Maternal Inflammation

BACKGROUND: Maternal immune activation (MIA) during prenatal life is a risk factor for neurodevelopmental disorders including schizophrenia and autism. Such conditions are associated with alterations in fronto-subcortical circuits, but their molecular basis is far from clear. METHODOLOGY/PRINCIPAL FINDINGS: Using two-dimensional differential in-gel electrophoresis (2D-DIGE) and mass spectrometry, with targeted western blot analyses for confirmation, we investigated the impact of MIA on the prefrontal and striatal proteome from an established MIA mouse model generated in C57B6 mice, by administering the viral analogue PolyI:C or saline vehicle (control) intravenously on gestation day (GD) 9. In striatum, 11 proteins were up-regulated and 4 proteins were down-regulated in the PolyI:C mice, while 10 proteins were up-regulated and 7 proteins down-regulated in prefrontal cortex (PFC). These were proteins involved in the mitogen-activated protein kinase (MAPK) signaling pathway, oxidation and auto-immune targets, including dual specificity mitogen-activated protein kinase kinase 1 (MEK), eukaryotic initiation factor (eIF) 4A-II, creatine kinase (CK)-B, L-lactate dehydrogenase (LDH)-B, WD repeat-containing protein and NADH dehydrogenase in the striatum; and guanine nucleotide-binding protein (G-protein), 14-3-3 protein, alpha-enolase, olfactory maker protein and heat shock proteins (HSP) 60, and 90-beta in the PFC. CONCLUSIONS/SIGNIFICANCE: This data fits with emerging evidence for disruption of critical converging intracellular pathways involving MAPK pathways in neurodevelopmental conditions and it shows considerable overlap with protein pathways identified by genetic modeling and clinical post-mortem studies. This has implications for understanding causality and may offer potential biomarkers and novel treatment targets for neurodevelopmental conditions

Beacon-integrated Attendance App

Taking attendance in class is still practised in many institutions of higher learning. With the advent of technologies, the conventional practice with pen-and-paper is deemed inefficient and time consuming. On top of the possible human-error in taking the attendance and also the ease of tampering with the data, manually taking attendance is very time-consuming. There are many apps developed to tackle these issues. However, many of these apps merely transform the practice from physical pen-and-paper to electronic touch-and-click in which the attendance is still taken by the instructor. In this paper, we propose the use of a beacon device to verify the attendance and it can be configured for automatic attendance taking. On top of that, other functionalities such as attendance report, submission of letter of absent, assign demonstrator/tutor to take attendance, manual attendance for those without smartphones are also included