Ten missteps in the management of inflammatory bowel disease in Asia: An expert report by the Asian Pacific Association of Gastroenterology Working Group on Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) is rapidly emerging in the Asia Pacific region. However, there are many challenges in the diagnosis and management of this condition. The Asian Pacific Association of Gastroenterology (APAGE) Working Group on IBD conducted a round table meeting to identify 10 common mistakes in the management of IBD in Asia. To summarize, many physicians still over rely on a definitive histological diagnosis before starting treatment and do not fully establish disease extent such as perianal and proximal gastrointestinal involvement in Crohn's disease (CD) or extent of involvement in ulcerative colitis (UC). It is also essential to actively look for evidence of extra-intestinal manifestations, which may influence choice of therapy. In terms of conventional therapy, underuse of topical 5 aminosalicylates (5-ASAs) in UC and inappropriate dosing of corticosteroids are also important considerations. Acute severe UC remains a life-threatening condition and delay in starting rescue therapy after inadequate response to intravenous steroids is still common. Anti-tumor necrosis factors should be considered first line in all cases of complex perianal fistulizing CD. Most patients with IBD are on potent immunosuppressive therapy and should be screened for latent infections and offered vaccinations according to guidelines. Under-recognition and management of significant complications such as anemia, osteoporosis, malnutrition, and thromboembolism should also be addressed. Colonoscopy is still not properly performed for dysplasia/cancer surveillance and for evaluating post-op recurrence of CD. Another common misstep is inappropriate withdrawal of medications during pregnancy leading to increased complications for the mother and the newborn.

Using Large Language Models to Understand Suicidality in a Social Media-Based Taxonomy of Mental Health Disorders: Linguistic Analysis of Reddit Posts.

Background: Rates of suicide have increased by over 35% since 1999. Despite concerted efforts, our ability to predict, explain, or treat suicide risk has not significantly improved over the past 50 years. Objective: The aim of this study was to use large language models to understand natural language use during public web-based discussions (on Reddit) around topics related to suicidality. Methods: We used large language model-based sentence embedding to extract the latent linguistic dimensions of user postings derived from several mental health-related subreddits, with a focus on suicidality. We then applied dimensionality reduction to these sentence embeddings, allowing them to be summarized and visualized in a lower-dimensional Euclidean space for further downstream analyses. We analyzed 2.9 million posts extracted from 30 subreddits, including r/SuicideWatch, between October 1 and December 31, 2022, and the same period in 2010. Results: Our results showed that, in line with existing theories of suicide, posters in the suicidality community (r/SuicideWatch) predominantly wrote about feelings of disconnection, burdensomeness, hopeless, desperation, resignation, and trauma. Further, we identified distinct latent linguistic dimensions (well-being, seeking support, and severity of distress) among all mental health subreddits, and many of the resulting subreddit clusters were in line with a statistically driven diagnostic classification system-namely, the Hierarchical Taxonomy of Psychopathology (HiTOP)-by mapping onto the proposed superspectra. Conclusions: Overall, our findings provide data-driven support for several language-based theories of suicide, as well as dimensional classification systems for mental health disorders. Ultimately, this novel combination of natural language processing techniques can assist researchers in gaining deeper insights about emotions and experiences shared on the web and may aid in the validation and refutation of different mental health theories.

Smartphone keyboard dynamics predict affect in suicidal ideation.

While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (ß = -0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data.

Temporal Alterations in White Matter in An App Knock-In Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) is the most common form of dementia and results in neurodegeneration and cognitive impairment. White matter (WM) is affected in AD and has implications for neural circuitry and cognitive function. The trajectory of these changes across age, however, is still not well understood, especially at earlier stages in life. To address this, we used the AppNL-G-F/NL-G-F knock-in (APPKI) mouse model that harbors a single copy knock-in of the human amyloid precursor protein (APP) gene with three familial AD mutations. We performed in vivo diffusion tensor imaging (DTI) to study how the structural properties of the brain change across age in the context of AD. In late age APPKI mice, we observed reduced fractional anisotropy (FA), a proxy of WM integrity, in multiple brain regions, including the hippocampus, anterior commissure (AC), neocortex, and hypothalamus. At the cellular level, we observed greater numbers of oligodendrocytes in middle age (prior to observations in DTI) in both the AC, a major interhemispheric WM tract, and the hippocampus, which is involved in memory and heavily affected in AD, prior to observations in DTI. Proteomics analysis of the hippocampus also revealed altered expression of oligodendrocyte-related proteins with age and in APPKI mice. Together, these results help to improve our understanding of the development of AD pathology with age, and imply that middle age may be an important temporal window for potential therapeutic intervention.

From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression.

BACKGROUND: Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was to investigate current digital phenotyping features and methods used in MDD. METHODS: We searched PubMed, PsycINFO, Embase, Scopus and Web of Science (10/11/2023) for articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk of bias was assessed using several sources. Studies were compared within analysis goals (correlating features with depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) were included. RESULTS: Studies achieved moderate performance. Common themes included challenges from complex and missing data (leading to a risk of bias), and a lack of external validation. DISCUSSION: Studies made progress towards relating digital phenotypes to clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may be beneficial for patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4.

Default Mode Network Hypoalignment of Function to Structure Correlates With Depression and Rumination.

BACKGROUND: Recent studies have begun to examine how signals in the brain correspond to the underlying white matter structure using tools from the field of graph signal processing to quantify brain function alignment to brain network topology. Here, we applied this framework for the first time toward a transdiagnostic cohort of individuals with internalizing psychopathologies, including mood and anxiety disorders, to uncover how such alignment within the default mode network (DMN) is related to depression and rumination symptoms. METHODS: Both diffusion-weighted and resting-state functional magnetic resonance imaging were obtained from participants at baseline (n = 60 patients, n = 19 healthy control participants). Patients were randomized to 12 weeks of treatment with either a selective serotonin reuptake inhibitor or cognitive behavioral therapy, and symptom scales were readministered posttreatment (n = 46 patients at follow-up). Using graph signal processing methodology, we quantified the alignment of functional signals to their underlying white matter structural networks. RESULTS: We found that signal alignment within the posterior DMN was decreased in patients with internalizing psychopathologies compared with healthy control participants and was inversely (negatively) correlated with baseline depression and rumination scales. Signal alignment within the posterior DMN was also correlated with the ratio of total within-DMN to extra-DMN functional connectivity for these regions. CONCLUSIONS: These findings are consistent with previous literature regarding pathological promiscuity of posterior DMN connectivity and provide the first graph signal processing-based analyses in a transdiagnostic cohort of patients with internalizing psychopathologies.

Primary antibiotic resistance of Helicobacter pylori in the Asia-Pacific region between 1990 and 2022: an updated systematic review and meta-analysis.

BACKGROUND: We previously showed rising primary antibiotic resistance of Helicobacter pylori during 1990-2015 in the Asia-Pacific region. However, whether primary antibiotic resistance continues to rise is unknown. Therefore, we aimed to assess the latest prevalence of H pylori antibiotic resistance in this region. METHODS: We did an updated systematic review and meta-analysis of observational studies and randomised controlled trials published in PubMed, Embase, and Cochrane Library between Jan 1, 1990, and July 12, 2023. Studies investigating primary H pylori resistance to clarithromycin, metronidazole, levofloxacin, amoxicillin, or tetracycline in individuals naive to eradication therapy in the Asia-Pacific region (as defined by the UN geoscheme) were eligible for inclusion. There were no language restrictions. Studies that focused on specific subpopulations (eg, children) were excluded. Using a standardised extraction form, two authors independently reviewed and extracted summary data from all eligible articles. The updated prevalence of antibiotic resistance was generated by meta-analysis under a random-effects model and subgroup analyses were done by countries and periods of study. Between-study variability was assessed by use of I2. The study is registered in PROSPERO, CRD42022339956. FINDINGS: A total of 351 studies, including 175 new studies and 176 studies from our previous analysis, were included in this meta-analysis. The overall prevalence of primary antibiotic resistance of H pylori between 1990 and 2022 was 22% (95% CI 20-23; I2=96%) for clarithromycin, 52% (49-55; I2=99%) for metronidazole, 26% (24-29; I2=96%) for levofloxacin, 4% (3-5; I2=95%) for tetracycline, and 4% (3-5; I2=95%) for amoxicillin. Prevalence varied considerably between countries and across study periods. From 1990 to 2022, the prevalence of primary resistance increased for clarithromycin, metronidazole, and levofloxacin but remained stable for amoxicillin and tetracycline. The latest primary resistance prevalences were 30% (95% CI 28-33; I2=93%) for clarithromycin, 61% (55-66; I2=99%) for metronidazole, 35% (31-39; I2=95%) for levofloxacin, 4% (2-6; I2=96%) for tetracycline, and 6% (4-8; I2=96%) for amoxicillin in the Asia-Pacific region. INTERPRETATION: Treatment guidelines should be adapted in response to the rising primary resistance of key antibiotics for H pylori eradication. A global policy to control and monitor the antibiotic resistance of H pylori is urgently needed. FUNDING: Ministry of Health and Welfare of Taiwan, National Science and Technology Council of Taiwan, and National Taiwan University. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Smartphone keyboard dynamics predict affect in suicidal ideation.

While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (ß = -0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data.

Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health.

We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs. not). The mood disorder group showed lower cognitive performance despite mild symptoms (depression/mania). There were also diurnal pattern differences with respect to cognitive performance: individuals with higher cognitive performance typed faster and were less sensitive to time of day. They also exhibited more well-defined diurnal patterns in smartphone keyboard usage: they engaged with the keyboard more during the day and tapered their usage more at night compared to those with lower cognitive performance, suggesting a healthier usage of their phone.

Disrupted Excitation-Inhibition Balance in Cognitively Normal Individuals at Risk of Alzheimer's Disease.

BACKGROUND: Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology. OBJECTIVE: Examine how AD risk factors (age, APOEɛ4, amyloid-ß) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures. METHODS: Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231). RESULTS: In absence of AD risk factors (APOEɛ4/Aß+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, ß= -0.007). Regression modeling including APOEɛ4 allele carriers (Aß-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, ß= 0.014), persisting with inclusion of Aß+ individuals (p = 0.012, ß= 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the Trail Making Test (p < 0.05). CONCLUSIONS: Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOEɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOEɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.

Disrupted excitation-inhibition balance in cognitively normal individuals at risk of Alzheimer's disease.

Background: Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology. Objective: Examine how AD risk factors (age, APOE-ɛ4, amyloid-ß) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures. Methods: Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231). Results: In absence of AD risk factors (APOE-ɛ4/Aß+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, ß = -0.007). Regression modeling including APOE-ɛ4 allele carriers (Aß-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, ß = 0.014), persisting with inclusion of Aß+ individuals (p = 0.012, ß = 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the trail-making test (p < 0.05). Conclusion: Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOE-ɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOE-ɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.

A Review on Smartphone Keystroke Dynamics as a Digital Biomarker for Understanding Neurocognitive Functioning.

Can digital technologies provide a passive unobtrusive means to observe and study cognition outside of the laboratory? Previously, cognitive assessments and monitoring were conducted in a laboratory or clinical setting, allowing for a cross-sectional glimpse of cognitive states. In the last decade, researchers have been utilizing technological advances and devices to explore ways of assessing cognition in the real world. We propose that the virtual keyboard of smartphones, an increasingly ubiquitous digital device, can provide the ideal conduit for passive data collection to study cognition. Passive data collection occurs without the active engagement of a participant and allows for near-continuous, objective data collection. Most importantly, this data collection can occur in the real world, capturing authentic datapoints. This method of data collection and its analyses provide a more comprehensive and potentially more suitable insight into cognitive states, as intra-individual cognitive fluctuations over time have shown to be an early manifestation of cognitive decline. We review different ways passive data, centered around keystroke dynamics, collected from smartphones, have been used to assess and evaluate cognition. We also discuss gaps in the literature where future directions of utilizing passive data can continue to provide inferences into cognition and elaborate on the importance of digital data privacy and consent.

A Malaysian consensus report on the diagnosis and treatment of Helicobacter pylori infection.

Helicobacter pylori is one of the most common chronic bacterial infections in humans and responsible for 90% of duodenal ulcers and 70-90% of gastric ulcers. Despite this, many misconceptions regarding the appropriate diagnostic method and treatment strategies still exist in the Malaysian clinical practice. Under the initiative of the steering committee, an expert panel consisting of nine key opinion leaders in the field of gastroenterology convened to develop a set of consensus statements that are relevant to the Malaysian healthcare practice. The panel members reviewed the current evidence on the management of H. pylori infection, focusing on the best practices that are relevant to the Malaysian population based on clinical experience and published clinical evidence. Using the modified Delphi method, the panel achieved consensus in three areas of H. pylori infection management: indications for testing, diagnosis, and treatment. The panel proposed a set of 19 consensus statements, which were synthesized via two rounds of blinded voting and group discussions. The recommendations provided are relevant to the Malaysian population and can be used as a guide by physicians across various healthcare settings to facilitate appropriate diagnostic testing and treatment of H. pylori infection.

The development of a novel, multilingual IBD knowledge questionnaire for Asian patients with inflammatory bowel disease.

BACKGROUND: Inflammatory bowel disease is an uncommon disease in developing nations whereby patient's knowledge on the disease may be limited. The CCKNOW questionnaire, a widely known questionnaire to assess patient's knowledge on the disease, may be too complex to comprehend for patients in developing countries. The aim of this study is to develop a new tool known as AIBDKQ questionnaire to evaluate the local inflammatory bowel disease patient's knowledge. METHODS: This was a prospective study carried out in four phases. In phase 1, three gastroenterologists with expertise in IBD generated a total of 21 questions related to the general knowledge of the disease in the English language. Phase 2 involved content and face validity whereby the questions were further validated by other gastroenterologists. In phase 3, the validated questions were translated into three languages namely Malay, Mandarin and Tamil which are commonly used in Malaysia. In phase 4 (statistical validity), administration of the questionnaires to patients and hospital staff were conducted to assess the construct validity, discriminative ability, predictive validity and reliability of the questionnaires. RESULTS: A total of 21 questions were generated initially. Further evaluation indicated that 20 items had adequate kappa and content validity index for relevance (CVI: 0.714 to 1, Kapp: 0.645 to 1) and clarity (CVI: 0.714 to 1, Kapp: 0.645 to 1). The questionnaires in four languages were administered to 213 patients to assess the construct validity. Six items were removed (three for low communality, one for small loading factors, two for cross loading), resulting in 16 final questions. Assessment with 34 hospital staff involving nurses, doctors and clerks showed significant differences in knowledge between the groups (F = 14.007, p < 0.001) and were able to discriminate doctors from nurses and clerks. Another group of 18 hospital staff administered with AIBDKQ and CCKNOW questionnaires showed a Pearson's correlation coefficient of 0.8 indicating strong correlation and concurrent predictive validity between the two questionnaires. Final assessment with 38 patients for reliability assessment revealed high intraclass correlation of the questionnaire among the four languages. CONCLUSIONS: The AIBDKQ has an excellent discriminant ability and internal consistency with a strong correlation when compared to the standard CCKNOW questionnaire.

Differential effects of transcranial current type on heart rate variability during emotion regulation in internalizing psychopathologies.

BACKGROUND: Internalizing psychopathologies (IPs) are characterized by disruptions in emotion regulation (ER). A potential target for ER modulation in individuals with IPs is the theta band. We hypothesized that offset theta-tACS (transcranial alternating current stimulation) would result in more enhanced ER, indexed by greater increase in heart rate variability (HRV), than transcranial direct current stimulation (tDCS) in participants with IPs. METHODS: This pilot study utilized a double-blind, pseudo-counterbalanced design. Participants with internalizing psychopathologies (anxiety and depression) were randomly assigned to receive either offset theta-tACS (n = 14) or tDCS (n = 15) and underwent four sessions of stimulation (two sham). In both arms, there were alternating iterations of an emotion regulation task (ERT) during or immediately after stimulation and rest. Heart rate data were collected during each ERT and rest iteration, and analyses were completed using high-frequency (HF) and root mean square of successive differences (RMSSD) HRV metrics. RESULTS: tACS participants consistently displayed increases in both HRV metrics from Time 1 to Time 4. Participants receiving tDCS displayed few significant changes in HF-HRV and no significant changes in RMSSD-HRV. LIMITATIONS: Due to the small sample size, analyses were limited. Additionally, the lack of a baseline ERT makes it difficult to determine overall ER improvement. CONCLUSIONS: tACS appears to increase ER capacity as reflected in increased HRV in individuals with internalizing psychopathologies, particularly after two sessions of stimulation. This study adds validity to the use of tACS as a neuromodulatory technique in cognitive and clinical research. Additional research is required to better understand potential carry-over effects of multiple sessions of stimulation.

Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes.

The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activity between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC well aligns with the observed FC when compared to that simulated with traditional structural connectome. Simulations were performed using the open source framework The Virtual Brain on High Performance Computing infrastructure.

A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity.

The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.

High Accuracy of a Simplified, Practical Algorithm in Differentiating Crohn's Disease from Intestinal Tuberculosis.

BACKGROUND: The differentiation between intestinal tuberculosis (ITB) and Crohn's disease (CD) remains a challenge, particularly in areas where tuberculosis is highly prevalent. Previous studies have identified features that favour one diagnosis over the other. The aim of the study was to determine the accuracy of a standardized protocol in the initial diagnosis of CD versus ITB. METHODS: All patients with suspected ITB or CD were prospectively recruited. A standardized protocol was applied, and the diagnosis was made accordingly. The protocol consists of history and examination, ileocolonoscopy with biopsies, and tuberculosis workup. The diagnosis of probable ITB was made based on at least one positive finding. All other patients were diagnosed as probable CD. Patients were treated either with anti-tubercular therapy or steroids. Reassessment was then carried out clinically, biochemically, and endoscopically. In patients with suboptimal response, the treatment was either switched or escalated depending on the reassessment. RESULTS: 164 patients were recruited with final diagnosis of 30 (18.3%) ITB and 134 (81.7%) CD. 1 (3.3%) out of 30 patients with ITB was initially treated as CD. 16 (11.9%) out of 134 patients with CD were initially treated as ITB. The initial overall accuracy for the protocol was 147/164 (89.6%). All patients received the correct diagnosis by 12 weeks after reassessment. CONCLUSION: In our population, most patients had CD rather than ITB. The standardized protocol had a high accuracy in differentiating CD from ITB.

Signed graph representation learning for functional-to-structural brain network mapping.

MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.