Brain activity in response to food images in patients with irritable bowel syndrome and functional dyspepsia

Brain activity during a public-speaking situation in virtual reality in patients with irritable bowel syndrome and functional dyspepsia

BACKGROUND

Psychosocial stress plays a central role in the pathophysiology of disorders of gut-brain interactions (DGBI), including functional dyspepsia (FD) and irritable bowel syndrome (IBS). Brain activity during psychosocial stress in patients with DGBI has not been adequately investigated. In this prospective study, we aimed to explore brain activity during psychosocial stress in patients with DGBI.

METHODS

Situations in an unmanned room, public space without attention, and public speaking were simulated in a virtual reality (VR) environment. Subjective stress, emotional state, and gastrointestinal (GI) symptoms were assessed using a visual analog scale, the State-Trait Anxiety Inventory, and the GI Symptom Rating Scale, respectively. Electrocardiograms were recorded to evaluate autonomic function. Activity in the prefrontal cortex (PFC) was examined using functional near-infrared spectroscopy (fNIRS).

RESULTS

Overall, 15 healthy controls, 15 patients with IBS, and 15 patients with FD were included. In the public-speaking scenario, subjective stress scores significantly decreased (indicating more stress) and sympathetic nervous activity increased equally among the three groups compared with those in an unmanned scene. Patients with IBS had higher activity in the left ventrolateral prefrontal cortex (VLPFC) and lower activity in the dorsolateral PFC (DLPFC) than those with FD and healthy controls.

CONCLUSIONS

Brain activity increased in the VLPFC and decreased in the DLPFC under stressful psychosocial situations created in the VR space in patients with IBS. Thus, the combination of VR and fNIRS is a viable option for evaluating brain activity under psychosocial stress in natural clinical settings.

Pattern Identification in Patients with Functional Dyspepsia Using Brain-Body Bio-Signals: Protocol of a Clinical Trial for AI Algorithm Development

Background: Functional dyspepsia (FD) is a common functional gastrointestinal disorder characterized by chronic digestive symptoms without identifiable structural abnormalities. FD affects approximately 8-46% of the population, leading to significant socioeconomic burdens due to reduced quality of life and productivity. Traditional medicine utilizes differential diagnosis through comprehensive examinations, which include observing and questioning, abdominal examination, and pulse diagnosis for functional gastrointestinal disorders. However, challenges persist in the standardization and objectivity of diagnostic protocols. Methods: This study aims to develop an artificial intelligence-based algorithm to predict identified patterns in patients with functional dyspepsia by integrating brain-body bio-signals, including brain activity measured by functional near-infrared spectroscopy, pulse wave, skin conductance response, and electrocardiography. We will conduct an observational cross-sectional study comprising 100 patients diagnosed according to the Rome IV criteria, collecting bio-signal data alongside differential diagnoses performed by licensed Korean medicine doctors. The study protocol was reviewed and approved by the Institutional Review Board of Kyung Hee University Hospital at Gangdong on 25 January 2024 (IRB no. KHNMCOH 2023-12-003-003) and was registered in the Korean Clinical Trial Registry (KCT0009275). Results: By creating AI algorithms based on bio-signals and integrating them into clinical practice, the objectivity and reliability of traditional diagnostics are expected to be enhanced. Conclusions: The integration of bio-signal analysis into the diagnostic process for patients with FD will improve clinical practices and support the broader acceptance of traditional-medicine diagnostic processes in healthcare.

Different Food Preferences in Patients with Gastrointestinal Disorders

Objective Gastrointestinal (GI) disorders such as functional dyspepsia (FD), irritable bowel syndrome (IBS), gastroesophageal reflux disease (GERD), and inflammatory bowel disease (IBD) can exhibit overlapping GI symptoms, including abdominal pain and alterations in bowel habits. The symptoms of GI disorders are commonly considered to be triggered and exacerbated by fatty food intake. Therefore, this study aimed to compare the food preferences of patients with GI disorders. Methods Forty food images (including fatty and light foods) and 20 animal images were selected to evaluate food preferences. The preference score was assessed using a visual analog scale ranging from 1 to 100. GI symptoms were evaluated using the GI Symptom Rating Scale (GSRS), and correlations between the GSRS and preference scores were investigated. Results Overall, 22 healthy controls and 23, 29, 27, and 20 patients with FD, IBS, GERD, and IBD, respectively, were enrolled. The preference score for all foods in patients with FD was significantly lower than that in healthy controls and those with IBS, GERD, and IBD (52.9 vs. 66.5 vs. 68.5 vs. 69.1 vs. 70.7, p<0.01). The score of fatty foods was lower in patients with FD than in healthy controls and those with IBS, GERD, and IBD (43.8 vs. 72.3 vs. 77.5 vs. 77.4 vs. 80.7, p<0.01), whereas that of light foods and animal images was not different among the groups. No significant correlation was found between the preference score and symptom severity. Conclusion Patients with FD had a negative preference for foods, particularly fatty foods, independent of the severity of GI symptoms.

Artificial Intelligence-Based Diagnostic Support System for Functional Dyspepsia Based on Brain Activity and Food Preference

Background and aim Disorders of gut-brain interaction (DGBI) are disorders where no organic clinical abnormalities are detected such as functional dyspepsia (FD) and irritable bowel syndrome (IBS). The brain activity of individuals with FD and IBS differs from that of healthy controls. Artificial intelligence can distinguish healthy controls from individuals with DGBI using several biomarkers. This study aimed to establish an artificial intelligence-based diagnostic support system using food preferences and brain activity in patients with DGBI. Methods ROME IV criteria were used to diagnose patients with FD and IBS. Their food preference was scored using a visual analog scale, and brain activity in the prefrontal cortex was investigated using functional near-infrared spectroscopy (fNIRS). The diagnostic model was developed based on the brain activity and visual analog scale scores for food using an artificial neural network model. Results Forty-one participants, including 25 patients with DGBI were enrolled in the study. The accuracy of the artificial intelligence-based diagnostic model using an artificial neural network in differentiating between healthy controls and patients with DGBI and between healthy controls and those with FD were 72.3% and 77.1%, respectively. Conclusions The artificial intelligence-based diagnostic model using brain activity and preference to food images showed sufficiently high accuracy in distinguishing patients with DGBI from healthy controls, and those with FD from healthy controls. Therefore, the fNIRS system provides objective evidence for diagnosing DGBI.