Psychiatry Research: Neuroimaging最新文献

{"title":"The interplay of genetic and environmental factors on the morphology of the limbic cortex and hippocampal subfields: Insights from an MRI twin study","authors":"Arsalan Vessal ,&nbsp;Amirreza Alijanpourotaghsara ,&nbsp;David Strelnikov ,&nbsp;Aliz Persely ,&nbsp;Marton Piroska ,&nbsp;Amirmasoud Alijanpour ,&nbsp;Zsofia Jokkel ,&nbsp;Laszlo Szalontai ,&nbsp;Bianka Forgo ,&nbsp;Lajos Rudolf Kozak ,&nbsp;Adam Bekesy-Szabo ,&nbsp;Pal Maurovich-Horvat ,&nbsp;David Laszlo Tarnoki ,&nbsp;Adam Domonkos Tarnoki","doi":"10.1016/j.pscychresns.2024.111909","DOIUrl":"10.1016/j.pscychresns.2024.111909","url":null,"abstract":"<div><h3>Introduction</h3><div>The limbic system and the hippocampus are complex brain structures with key roles in memory, emotions, sexual stimulation and learning, with subregion abnormalities associated with a range of disorders and psychopathologies. Our study aimed to explore the heritability of specific subfield structures within the limbic system and hippocampus first in a Caucasian twin sample with volBrain pipeline.</div></div><div><h3>Materials and Methods</h3><div>59 healthy adult Caucasian twin pairs from the Hungarian Twin Registry without any history of previous neurodegenerative or cerebrovascular diseases underwent brain MRI on a 3.0 T scanner (43 monozygotic, MZ and 16 dizygotic, DZ pairs, with a median age of 50±+27 years). The volBrain automated volumetry pipeline was used to calculate the subcortical and general brain volumes from three-dimensional T1-weighted images. Based on age- and sex-adjusted MZ and DZ intra-pair correlations, the univariate ACE model was applied to calculate additive genetic, shared and unshared environmental influences.</div></div><div><h3>Results</h3><div>Adjusting for age and sex, moderate to strong heritability (A: 59.7 to 73.1 %) was found for most limbic cortex volumes, except for the volumes of entorhinal area and posterior cingulate gyrus where common environmental contribution was detected (C: 56.6 % and 65.0 %, respectively). A substantial heritability (A: 67.0 to 79.4 %) was estimated for the overall hippocampus and most subfield volumes, except for the CA2-CA3 region which was determinated by common environmental factors (C: 45.7 %). Unique environmental variance was a minor to moderate contributor across all variables (E: 20.6 to 54.3 %).</div></div><div><h3>Conclusions</h3><div>Albeit most limbic cortex, overall hippocampus and most subfield volumes are under substantial genetic influence in healthy adult twins, the volumes of entorhinal area, posterior cingulate gyrus and the CA2-CA3 region of the hippocampus are influenced common environmental factors. The findings underline the importance of unique environmental factors which may play a role in the prevention of disorders related to limbic cortex and hippocampus.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"345 ","pages":"Article 111909"},"PeriodicalIF":2.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning based classification of excessive smartphone users via neuronal cue reactivity.","authors":"Jailan Oweda, Mike Michael Schmitgen, Gudrun M Henemann, Marius Gerdes, Robert Christian Wolf","doi":"10.1016/j.pscychresns.2024.111903","DOIUrl":"https://doi.org/10.1016/j.pscychresns.2024.111903","url":null,"abstract":"<p><p>Excessive Smartphone Use (ESU) poses a significant challenge in contemporary society, yet its recognition as a distinct disorder remains ambiguous. This study aims to address this gap by leveraging functional magnetic resonance imaging (fMRI) data and machine learning techniques to classify ESU and non-excessive smartphone users (n-ESU) based on their neural Cue-Reactivity (CR) signatures. By conducting a CR task and analyzing brain activation patterns, we identified spatial similarities between addictive smartphone use and established addictive disorders. Our approach involved employing Support Vector Machines (SVM) for classification, enhanced with feature selection methods such as Recursive Feature Elimination (RFE) and Model-based Selection and dimensionality reduction methods such as and Principal Component Analysis (PCA) to mitigate the challenges posed by limited dataset size and high dimensionality of fMRI data. The results demonstrate the effectiveness of our classification model, achieving accuracies of up to 79.9 %. Furthermore, we observed region-specific activations contributing significantly to classification accuracy, highlighting the potential biomarkers associated with ESU. External validation on longitudinal data revealed the necessity for larger training datasets to improve model generalizability. Additionally, feature selection techniques proved crucial for optimizing model performance, particularly in datasets with combined information from multiple sources. Our findings underscore the importance of incorporating more data to enhance model stability and generalizability, with implications for advancing the understanding and treatment of ESU and related disorders. Overall, our study demonstrates the promise of machine learning approaches in elucidating neural correlates of ESU and informing targeted interventions for affected individuals.</p>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":" ","pages":"111903"},"PeriodicalIF":2.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142473275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-based deep learning for differentiating between bipolar and major depressive disorders","authors":"Ruipeng Li ,&nbsp;Yueqi Huang ,&nbsp;Yanbin Wang ,&nbsp;Chen Song ,&nbsp;Xiaobo Lai","doi":"10.1016/j.pscychresns.2024.111907","DOIUrl":"10.1016/j.pscychresns.2024.111907","url":null,"abstract":"<div><div>Mood disorders, particularly bipolar disorder (BD) and major depressive disorder (MDD), manifest changes in brain structure that can be detected using structural magnetic resonance imaging (MRI). Although structural MRI is a promising diagnostic tool, prevailing diagnostic criteria for BD and MDD are predominantly subjective, sometimes leading to misdiagnosis. This challenge is compounded by a limited understanding of the underlying causes of these disorders. In response, we present SE-ResNet, a Residual Network (ResNet)-based framework designed to discriminate between BD, MDD, and healthy controls (HC) using structural MRI data. Our approach extends the traditional Squeeze-and-Excitation (SE) layer by incorporating a dedicated branch for spatial attention map generation, equipped with soft-pooling, a 7 × 7 convolution, and a sigmoid function, intended to detect complex spatial patterns. The fusion of channel and spatial attention maps through element-wise addition aims to enhance the model's ability to discriminate features. Unlike conventional methods that use max-pooling for downsampling, our methodology employs soft-pooling, which aims to preserve a richer representation of input features and reduce data loss. When evaluated on a proprietary dataset comprising 303 subjects, the SE-ResNet achieved an accuracy of 85.8 %, a recall of 85.7 %, a precision of 85.9 %, and an F1 score of 85.8 %. These performance metrics suggest that the SE-ResNet framework has potential as a tool for detecting psychiatric disorders using structural MRI data.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"345 ","pages":"Article 111907"},"PeriodicalIF":2.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142366350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"(Interfering) Cortical mechanisms of standing balance and cognition in old-age depression: A functional near-infrared spectroscopy (fNIRS) study","authors":"Pia Thönnessen ,&nbsp;L.Cornelius Bollheimer ,&nbsp;Michael Luehrs ,&nbsp;Ute Habel ,&nbsp;Bettina Sorger ,&nbsp;Charlotte Huppertz","doi":"10.1016/j.pscychresns.2024.111905","DOIUrl":"10.1016/j.pscychresns.2024.111905","url":null,"abstract":"<div><div>Major depressive disorder in old age can cause changes in the cerebral cortex that might lead to postural imbalance and thus increase fall risk. We aim to examine cortical activation during standing balance in depressed older patients compared to healthy controls and to determine how an additional cognitive task affects this activation. Eleven older patients (age ≥65 years) diagnosed with major depressive disorder and sixteen age-matched healthy controls participated in the study. Functional near-infrared spectroscopy (fNIRS) was used to assess cortical activation of the prefrontal (PFC) and motor (MC) cortex during standing balance with eyes closed under single and dual task (counting backwards). The present study generally revealed tendencies in the MC – and partly the PFC too – for more activation whilst balancing compared to baseline. Also, in the MC, patients tended to show more cortical activation compared to controls and dual task tended to elicit more activation. The results suggest that depressed older patients, to compensate for their illness, may require increased cortical activation to perform motor and cognitive tasks than healthy controls. The absence of PFC activation in the main analyses may be related to the small participant number and possibly to too simple task conditions.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"345 ","pages":"Article 111905"},"PeriodicalIF":2.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Longitudinal changes in neural responses to fearful faces in adolescents with anorexia nervosa – A fMRI study","authors":"Lukas Stanetzky ,&nbsp;Arne Hartz ,&nbsp;Kimberly Buettgen ,&nbsp;Brigitte Dahmen ,&nbsp;Beate Herpertz-Dahlmann ,&nbsp;Kerstin Konrad ,&nbsp;Jochen Seitz","doi":"10.1016/j.pscychresns.2024.111904","DOIUrl":"10.1016/j.pscychresns.2024.111904","url":null,"abstract":"<div><h3>Objective</h3><div>Although proven neuronal changes are correlated with anorexia nervosa (AN), where these changes occur and how they change during the course of this disease are often unclear; this is especially true regarding emotion processing, e.g., of anxiety, despite a growing body of literature on its importance for the pathophysiology and clinical course of patients with AN.</div></div><div><h3>Methods</h3><div>Twenty-two female adolescent patients with AN were scanned during acute starvation and after short-term weight recovery and were compared to 27 healthy controls. A well-established face-matching paradigm involving individuals with different emotions was used during fMRI.</div></div><div><h3>Results</h3><div>Patients with AN selectively showed significantly increasing neural activation in the somatomotor cortex when viewing fearful faces following short-term weight recovery. No differences were found compared to healthy controls or for neutral, angry or surprised faces. Neural activation in response to fearful faces during acute starvation was associated with lower BMI-SDS and greater illness burden.</div></div><div><h3>Conclusion</h3><div>Higher somatomotor activity could represent anxiety-induced preparations for motor reactions (e.g., fight or flight) that are more pronounced in more affected patients. These results align with recent models of AN that increasingly incorporate anxiety into the pathophysiological and prognostic model of AN and help elucidate its underlying neurological mechanisms.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"345 ","pages":"Article 111904"},"PeriodicalIF":2.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered resting-state and dynamic functional connectivity of hypothalamic in first-episode depression: A functional magnetic resonance imaging study","authors":"Xue Xiao ,&nbsp;Jifei Sun ,&nbsp;Jing Tian ,&nbsp;Xu Sun ,&nbsp;Chunhong Yang ,&nbsp;Ying Hao ,&nbsp;Yanan Zhao ,&nbsp;Xue Yu ,&nbsp;Mingshan Li ,&nbsp;Shaoyuan Li ,&nbsp;Jiliang Fang ,&nbsp;Xiaobing Hou","doi":"10.1016/j.pscychresns.2024.111906","DOIUrl":"10.1016/j.pscychresns.2024.111906","url":null,"abstract":"<div><div>The hypothalamus is an important component of the hypothalamic-pituitary-adrenal axis and an important brain region of the limbic system. Twenty-four first depressive episode(FDE) patients and 25 healthy controls were recruited for this study. The hypothalamus was used as a seed to observe the characteristics of resting state and dynamic functional connectivity (FC) changes in FDE patients, and further observed the correlation between the different brain regions and clinical symptoms. The results found that compared with the HC group, the FDE group showed sFC was increased of the left hypothalamus with right superior parietal gyrus and right middle temporal gyrus, and dFC was increased of the left hypothalamus with left inferior occipital gyrus. And sFC was increased of the right hypothalamus with right orbital part of inferior frontal gyrus, right supplementary motor area, and right middle temporal gyrus, and the dFC was also increased of right hypothalamus with right superior parietal gyrus and left middle temporal gyrus. In addition,there was a negative correlation between dFC values of the right hypothalamus with the right superior parietal gyrus and clinical symptoms in the FDE group. This study provides new insights into understanding the altered neuropathological mechanisms of the hypothalamic circuit in FDE.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"345 ","pages":"Article 111906"},"PeriodicalIF":2.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142352679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods","authors":"Xiaowen Xu ,&nbsp;Yang Li ,&nbsp;Ning Ding ,&nbsp;Yukun Zang ,&nbsp;Shanshan Sun ,&nbsp;Gaoyu Shen ,&nbsp;Xiufeng Song","doi":"10.1016/j.pscychresns.2024.111901","DOIUrl":"10.1016/j.pscychresns.2024.111901","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>To explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine learning (ML) methods in combination with structural magnetic resonance imaging (sMRI) features.</div></div><div><h3>Methods</h3><div>A total of 60 ASD children and 48 age- and sex-matched typically developing (TD) children were prospectively enrolled from January 2023 to April 2024. All subjects were scanned using 3D-T1 sequences. Automated brain segmentation techniques were utilized to obtain the standardized volume of each brain structure (the ratio of the absolute volume of brain structure to the whole brain volume). The standardized volumes of each brain structure in the two groups were statistically compared, and the volume data of brain areas with significant differences were combined with ML methods to diagnose and predict ASD patients.</div></div><div><h3>Results</h3><div>Compared with the TD group, the volumes of the right lateral orbitofrontal cortex, right medial orbitofrontal cortex, right pars opercularis, right pars triangularis, left hippocampus, bilateral parahippocampal gyrus, left fusiform gyrus, right superior temporal gyrus, bilateral insula, bilateral inferior parietal cortex, right precuneus cortex, bilateral putamen, left pallidum, and right thalamus were significantly increased in the ASD group (<em>P</em>&lt; 0.05). Among six ML algorithms, support vector machine (SVM) and adaboost (AB) had better performance in differentiating subjects with ASD from those TD children, with their average area under curve (AUC) reaching 0.91 and 0.92, respectively.</div></div><div><h3>Conclusion</h3><div>Automatic brain segmentation technology based on artificial intelligence can rapidly and directly measure and display the volume of brain structures in children with autism spectrum disorder and typically developing children. Children with ASD show abnormalities in multiple brain structures, and when paired with sMRI features, ML algorithms perform well in the diagnosis of ASD.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"345 ","pages":"Article 111901"},"PeriodicalIF":2.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized fMRI tasks for grief severity in bereaved individuals: Emotional counting Stroop and grief elicitation protocols","authors":"Jonathan Singer ,&nbsp;Joseph S. Goveas ,&nbsp;Lauren Elliott ,&nbsp;Harshit Parmar ,&nbsp;O'Connor Mary-Frances","doi":"10.1016/j.pscychresns.2024.111902","DOIUrl":"10.1016/j.pscychresns.2024.111902","url":null,"abstract":"<div><p>Approximately 7–10% of people experiencing bereavement following a death develop prolonged grief disorder, a psychiatric disorder included in the DSM-5-TR. Prolonged grief disorder encompasses core symptoms of intense yearning/longing for and preoccupation with thoughts or memories of the deceased person experienced to a clinically significant degree for at least the last month, other key associated symptoms (e.g., avoidance, emotional pain), and the death must have occurred at least one year prior to diagnosis. Extant research has shown a relationship between activation in the reward pathway (e.g., nucleus accumbens) and grief severity. To date, functional MRI studies have primarily utilized the Emotional Counting Stroop task (ecStroop) and the Grief Elicitation task to explore these relationships. However, these prior studies are not without limitations, including small sample sizes and absence of a unified task protocol, hindering meaningful comparisons between studies. This protocol paper describes the ecStroop task and the Grief Elicitation task, which will be vital for facilitating multisite studies and enabling comparisons across studies. This will aid to advance the field by identifying neurophysiological measures that may, in the future, serve as potential biomarkers of prolonged grief disorder.</p></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"345 ","pages":"Article 111902"},"PeriodicalIF":2.1,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of RNFL and macular changes in the eye related to multiple substance use using OCT","authors":"Şüheda Kaya ,&nbsp;Mehmet Kaan Kaya","doi":"10.1016/j.pscychresns.2024.111889","DOIUrl":"10.1016/j.pscychresns.2024.111889","url":null,"abstract":"<div><p>Substance use is a chronic and recurrent public healthcare concern increasing in the world, causing negative outcomes. Two or more substance use is common among people who have substance use disorders and who receive treatment. For this reason, the present study aimed to measure Retinal Nerve Fiber Layer (RNFL), Mean Macular Thickness (MMT), Central Macular Thickness (CMT) in patients who have Multiple substance use disorder (MSUD) using Optical Coherence Tomography (OCT), considering that it will contribute to the literature. Among the inpatients who were rehabilitated in Elazig Mental Hospital Alcohol and Substance Addiction Treatment Center, 75 people who were diagnosed with MSUD according to DSM-5 and met the criteria, and 51 control groups were included in the study. RNFL, MMT and CMT measurements of both eyes of all participants were made by using the OCT. Total RNFL measurement were significantly thicker than the control group (<em>p</em> &lt; 0.001). MMT and CMT of the eyes of the patient were thinner than the control group (<em>p</em> = 0.009, <em>p</em> &lt; 0.001). The findings provide important contributions to the literature data and in light of these findings, it can be recommended to consider visual findings and possible neurodegeneration when evaluating patients in the addiction group and planning their treatment.</p></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"345 ","pages":"Article 111889"},"PeriodicalIF":2.1,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroimaging-based drug discovery for amyloid clearance therapy in Alzheimer's disease using validated causation analysis","authors":"Anindita Bhattacharjee ,&nbsp;Pratik Purohit ,&nbsp;Prasun K. Roy","doi":"10.1016/j.pscychresns.2024.111890","DOIUrl":"10.1016/j.pscychresns.2024.111890","url":null,"abstract":"<div><div>Aging-induced hepatic dysfunction can impair cholesterol metabolism, reducing the availability of cholic acid (CA, bile-acid) in brain. CA is reported to have neuroprotective characteristics in preclinical investigations of Alzheimer's disease (AD). Our aim is to probe the causal-connectivity between the players: amyloid, cholic acid and cerebral-blood-flow, and thereby explore therapeutic applicability in AD. From AD neuroimaging initiative biospecimen platform, we evaluated serum cholic-acid (182 healthy/136 AD individuals). We also assessed 50 healthy/50 Alzheimer's subjects containing MRI-ASL scanning (cerebral blood-flow, CBF) and PET-AV45 scanning (amyloid-load). We performed computational causal connectivity to determine the cause-effect relationship among the parameters. Serum cholic acid in AD subjects substantially decreased to half of controls. Causal-connectivity revealed two novel causative pathways: (i) Decreasing serum CA markedly increased amyloid-load; (ii) Increasing amyloid-load distinctly decreased CBF. We substantiated these two causation pathways respectively with collateral available preclinical observations: (a) increased cholic acid reduces amyloid formation by diminishing gamma-secretase; (b) this decreased amyloid induces capillary-flow enhancement by relaxing vascular pericytes. Indeed, cholic acid can increase amyloid-clearance factor. Neuroimaging-based causal connectivity analysis showed that repositioned pharmacological modulation by cholate derivatives may have appreciable potential as novel window for therapeutic approach to AD. Indicative clinical validation is furnished from available therapeutic trial leads.</div></div>","PeriodicalId":20776,"journal":{"name":"Psychiatry Research: Neuroimaging","volume":"345 ","pages":"Article 111890"},"PeriodicalIF":2.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}