Altered intrinsic functional brain architecture in patients with functional constipation: a surface-based network study

BackgroundFunctional constipation (FCon) is a common functional gastrointestinal disorder (FGID). Studies have indicated a higher likelihood of psychiatric disorders, such as anxiety, depression, sleep disturbances, and impaired concentration, among patients with FCon. However, the underlying pathophysiological mechanisms responsible for these symptoms in FCon patients remain to be fully elucidated. The human brain is a complex network architecture with several fundamental organizational properties. Neurological interactions between gut symptoms and psychiatric issues may be closely associated with these complex networks.MethodsIn the present study, a total of 35 patients with FCon and 40 healthy controls (HC) were recruited for a series of clinical examinations and resting-state functional magnetic imaging (RS-fMRI). We employed the surface-based analysis (SBA) approach, utilizing the Schaefer cortical parcellation template and Tikhonov regularization. Graph theoretical analysis (GTA) and functional connectivity (FC) analysis of RS-fMRI were conducted to investigate the aberrant network alterations between the two groups. Additionally, correlation analyses were performed between the network indices and clinical variables in patients with FCon.ResultsAt the global level, we found altered topological properties and networks in patients with FCon, mainly including the significantly increased clustering coefficient (CP), local efficiency (Eloc), and shortest path length (LP), whereas the decreased global efficiency (Eglob) compared to HC. At the regional level, patients with FCon exhibited increased nodal efficiency in the frontoparietal network (FPN). Furthermore, FC analysis demonstrated several functional alterations within and between the Yeo 7 networks, particularly including visual network (VN), limbic network (LN), default mode network (DMN), and somatosensory-motor network (SMN) in sub-network and large-scale network analysis. Correlation analysis revealed that there were no significant associations between the network metrics and clinical variables in the present study.ConclusionThese results highlight the altered topological architecture of functional brain networks associated with visual perception abilities, emotion regulation, sensorimotor processing, and attentional control, which may contribute to effectively targeted treatment modalities for patients with FCon

Associations of CAIDE Dementia Risk Score with MRI, PIB-PET measures, and cognition

Background: CAIDE Dementia Risk Score is the first validated tool for estimating dementia risk based on a midlife risk profile. Objectives: This observational study investigated longitudinal associations of CAIDE Dementia Risk Score with brain MRI, amyloid burden evaluated with PIB-PET, and detailed cognition measures. Methods: FINGER participants were at-risk elderly without dementia. CAIDE Risk Score was calculated using data from previous national surveys (mean age 52.4 years). In connection to baseline FINGER visit (on average 17.6 years later, mean age 70.1 years), 132 participants underwent MRI scans, and 48 underwent PIB-PET scans. All 1,260 participants were cognitively assessed (Neuropsychological Test Battery, NTB). Neuroimaging assessments included brain cortical thickness and volumes (Freesurfer 5.0.3), visually rated medial temporal atrophy (MTA), white matter lesions (WML), and amyloid accumulation. Results: Higher CAIDE Dementia Risk Score was related to more pronounced deep WML (OR 1.22, 95% CI 1.05-1.43), lower total gray matter (beta- coefficient -0.29, p = 0.001) and hippocampal volume (beta- coefficient -0.28, p = 0.003), lower cortical thickness (beta-coefficient -0.19, p = 0.042), and poorer cognition (beta-coefficients -0.31 for total NTB score, -0.25 for executive functioning, -0.33 for processing speed, and -0.20 for memory, all p <0.001). Higher CAIDE Dementia Risk Score including APOE genotype was additionally related to more pronounced MTA (OR 1.15,95% CI 1.00-1.30). No associations were found with periventricular WML or amyloid accumulation. Conclusions: The CAIDE Dementia Risk Score was related to indicators of cerebrovascular changes and neurodegeneration on MRI, and cognition. The lack of association with brain amyloid accumulation needs to be verified in studies with larger sample sizes.Peer reviewe

White Matter Changes on Diffusion Tensor Imaging in the FINGER Randomized Controlled Trial

Background: Early pathological changes in white matter microstructure can be studied using the diffusion tensor imaging (DTI). It is not only important to study these subtle pathological changes leading to cognitive decline, but also to ascertain how an intervention would impact the white matter microstructure and cognition in persons at-risk of dementia.Objectives: To study the impact of a multidomain lifestyle intervention on white matter and cognitive changes during the 2-year Finnish Geriatric Intervention Study to prevent Cognitive Impairment and Disability (FINGER), a randomized controlled trial in at-risk older individuals (age 60-77 years) from the general population.Methods: This exploratory study consisted of a subsample of 60 FINGER participants. Participants were randomized to either a multidomain intervention (diet, exercise, cognitive training, and vascular risk management, n = 34) or control group (general health advice, n = 26). All underwent baseline and 2-year brain DTI. Changes in fractional anisotropy (FA), diffusivity along domain (F1) and non-domain (F2) diffusion orientations, mean diffusivity (MD), axial diffusivity (AxD), radial diffusivity (RD), and their correlations with cognitive changes during the 2-year multidomain intervention were analyzed.Results: FA decreased, and cognition improved more in the intervention group compared to the control group (p < 0.05), with no significant intergroup differences for changes in F1, F2, MD, AxD, or RD. The cognitive changes were significantly positively related to FA change, and negatively related to RD change in the control group, but not in the intervention group.Conclusion: The 2-year multidomain FINGER intervention may modulate white matter microstructural alterations

Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline

The importance of early interventions in Alzheimer's disease (AD) emphasizes the need to accurately and efficiently identify at-risk individuals. Although many dementia prediction models have been developed, there are fewer studies focusing on detection of brain pathology. We developed a model for identification of amyloid-PET positivity using data on demographics, vascular factors, cognition,APOEgenotype, and structural MRI, including regional brain volumes, cortical thickness and a visual medial temporal lobe atrophy (MTA) rating. We also analyzed the relative importance of different factors when added to the overall model. The model used baseline data from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) exploratory PET sub-study. Participants were at risk for dementia, but without dementia or cognitive impairment. Their mean age was 71 years. Participants underwent a brain 3T MRI and PiB-PET imaging. PiB images were visually determined as positive or negative. Cognition was measured using a modified version of the Neuropsychological Test Battery. Body mass index (BMI) and hypertension were used as cardiovascular risk factors in the model. Demographic factors included age, gender and years of education. The model was built using the Disease State Index (DSI) machine learning algorithm. Of the 48 participants, 20 (42%) were rated as A beta positive. Compared with the A beta negative group, the A beta positive group had a higher proportion ofAPOE epsilon 4 carriers (53 vs. 14%), lower executive functioning, lower brain volumes, and higher visual MTA rating. AUC [95% CI] for the complete model was 0.78 [0.65-0.91]. MRI was the most effective factor, especially brain volumes and visual MTA rating but not cortical thickness.APOEwas nearly as effective as MRI in improving detection of amyloid positivity. The model with the best performance (AUC 0.82 [0.71-0.93]) was achieved by combiningAPOEand MRI. Our findings suggest that combining demographic data, vascular risk factors, cognitive performance,APOEgenotype, and brain MRI measures can help identify A beta positivity. Detecting amyloid positivity could reduce invasive and costly assessments during the screening process in clinical trials