Neuroimage-Clinical最新文献

{"title":"Associations between low-moderate prenatal alcohol exposure and brain development in childhood","authors":"Deanne K. Thompson ,&nbsp;Claire E. Kelly ,&nbsp;Thijs Dhollander ,&nbsp;Evelyne Muggli ,&nbsp;Stephen Hearps ,&nbsp;Sharon Lewis ,&nbsp;Thi-Nhu-Ngoc Nguyen ,&nbsp;Alicia Spittle ,&nbsp;Elizabeth J. Elliott ,&nbsp;Anthony Penington ,&nbsp;Jane Halliday ,&nbsp;Peter J. Anderson","doi":"10.1016/j.nicl.2024.103595","DOIUrl":"10.1016/j.nicl.2024.103595","url":null,"abstract":"<div><h3>Background</h3><p>The effects of low-moderate prenatal alcohol exposure (PAE) on brain development have been infrequently studied.</p></div><div><h3>Aim</h3><p>To compare cortical and white matter structure between children aged 6 to 8 years with low-moderate PAE in trimester 1 only, low-moderate PAE throughout gestation, or no PAE.</p></div><div><h3>Methods</h3><p>Women reported quantity and frequency of alcohol consumption before and during pregnancy. Magnetic resonance imaging was undertaken for 143 children aged 6 to 8 years with PAE during trimester 1 only (n = 44), PAE throughout gestation (n = 58), and no PAE (n = 41). <em>T₁</em>-weighted images were processed using FreeSurfer, obtaining brain volume, area, and thickness of 34 cortical regions per hemisphere. Fibre density (FD), fibre cross-section (FC) and fibre density and cross-section (FDC) metrics were computed for diffusion images. Brain measures were compared between PAE groups adjusted for age and sex, then additionally for intracranial volume.</p></div><div><h3>Results</h3><p>After adjustments, the right caudal anterior cingulate cortex volume (<em>p</em>_FDR = 0.045) and area (<em>p</em>_FDR = 0.008), and right cingulum tract cross-sectional area (p_FWE &lt; 0.05) were smaller in children exposed to alcohol throughout gestation compared with no PAE.</p></div><div><h3>Conclusion</h3><p>This study reports a relationship between low-moderate PAE throughout gestation and cingulate cortex and cingulum tract alterations, suggesting a teratogenic vulnerability. Further investigation is warranted.</p></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213158224000342/pdfft?md5=5577d6a07bcc6ec27a7102de4be0170e&pid=1-s2.0-S2213158224000342-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140279675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeted non-invasive brain stimulation boosts attention and modulates contralesional brain networks following right hemisphere stroke","authors":"Elena Olgiati ,&nbsp;Ines R. Violante ,&nbsp;Shuler Xu ,&nbsp;Toby G. Sinclair ,&nbsp;Lucia M. Li ,&nbsp;Jennifer N. Crow ,&nbsp;Marianna E. Kapsetaki ,&nbsp;Roberta Calvo ,&nbsp;Korina Li ,&nbsp;Meenakshi Nayar ,&nbsp;Nir Grossman ,&nbsp;Maneesh C. Patel ,&nbsp;Richard J.S. Wise ,&nbsp;Paresh A. Malhotra","doi":"10.1016/j.nicl.2024.103599","DOIUrl":"10.1016/j.nicl.2024.103599","url":null,"abstract":"<div><p>Right hemisphere stroke patients frequently present with a combination of lateralised and non-lateralised attentional deficits characteristic of the neglect syndrome. Attentional deficits are associated with poor functional outcome and are challenging to treat, with non-lateralised deficits often persisting into the chronic stage and representing a common complaint among patients and families.</p><p>In this study, we investigated the effects of non-invasive brain stimulation on non-lateralised attentional deficits in right-hemispheric stroke. In a randomised double-blind sham-controlled crossover study, twenty-two patients received real and sham transcranial Direct Current Stimulation (tDCS) whilst performing a non-lateralised attentional task. A high definition tDCS montage guided by stimulation modelling was employed to maximise current delivery over the right dorsolateral prefrontal cortex, a key node in the vigilance network. In a parallel study, we examined brain network response to this tDCS montage by carrying out concurrent fMRI during stimulation in healthy participants and patients.</p><p>At the group level, stimulation improved target detection in patients, reducing overall error rate when compared with sham stimulation. TDCS boosted performance throughout the duration of the task, with its effects briefly outlasting stimulation cessation. Exploratory lesion analysis indicated that response to stimulation was related to lesion location rather than volume. In particular, reduced stimulation response was associated with damage to the thalamus and postcentral gyrus. Concurrent stimulation-fMRI revealed that tDCS did not affect local connectivity but influenced functional connectivity within large-scale networks in the contralesional hemisphere.</p><p>This combined behavioural and functional imaging approach shows that brain stimulation targeted to surviving tissue in the ipsilesional hemisphere improves non-lateralised attentional deficits following stroke. This effect may be exerted via contralesional network effects.</p></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221315822400038X/pdfft?md5=6e0a6436c19511d7b192a9f18c5dce21&pid=1-s2.0-S221315822400038X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140400070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral peak analysis and intrinsic neural timescales as markers for the state of consciousness","authors":"Ezequiel Pablo Espinosa ,&nbsp;Di Zang ,&nbsp;Andrea Buccellato ,&nbsp;Zengxin Qi ,&nbsp;Xuehai Wu ,&nbsp;Samira Abbasi ,&nbsp;Yasir Catal ,&nbsp;Stephan Lechner ,&nbsp;Federico Zilio ,&nbsp;Georg Northoff","doi":"10.1016/j.nicl.2024.103698","DOIUrl":"10.1016/j.nicl.2024.103698","url":null,"abstract":"<div><div>Resting state EEG in patients with disorders of consciousness (DOC) is characterized by an increase of power in the delta frequency band and a concurrent decrease in the alpha range, equivalent to a weakening or disappearance of the alpha peak. Prolongation of Intrinsic Neural Timescales (INTs) is also associated with DOCs. Together, this raises the question whether the decreased alpha peak relates to the prolonged INTs and, importantly, how that can be used for diagnosing the state of consciousness in DOC individuals. Analyzing resting state EEG recordings from both healthy subjects and DOC patients, we measure INTs through autocorrelation window (ACW) and utilize peak analysis to quantify the weakening of the alpha peak. First, we replicate previous findings of prolonged ACW in DOC patients. We then identify significantly lower alpha peak measures in DOC compared to controls. Interestingly, spectral peaks shift from the alpha to the theta range in several DOC subjects while such change is absent in healthy controls. Next, our study reveals a close relationship between ACW and alpha peak in both healthy and DOC subjects, a correlation that holds for theta peaks in DOC. Further, the prolonged ACW correlates with the state of consciousness, as quantified by the Coma Recovery Scale-Revised (CRS-R), and mediates the relationship between theta peak and CRS-R. Finally, through split analyses and machine learning, we show that ACW and alpha peak measures conjointly distinguish healthy controls and DOC patients with high accuracy (95.5%). In conclusion, we demonstrate that the prolongation of ACW, together with spectral peak measures, holds promise to serve as additional EEG biomarkers for diagnosing the state of consciousness in DOC subjects.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic enrichment for early-stage Huntington’s disease: An explainable machine learning approach for clinical trial","authors":"Mohsen Ghofrani-Jahromi ,&nbsp;Govinda R. Poudel ,&nbsp;Adeel Razi ,&nbsp;Pubu M. Abeyasinghe ,&nbsp;Jane S. Paulsen ,&nbsp;Sarah J. Tabrizi ,&nbsp;Susmita Saha ,&nbsp;Nellie Georgiou-Karistianis","doi":"10.1016/j.nicl.2024.103650","DOIUrl":"10.1016/j.nicl.2024.103650","url":null,"abstract":"<div><h3>Background</h3><p>In Huntington’s disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on <em>in vivo</em> brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process.</p></div><div><h3>Objectives</h3><p>To improve stratification of Huntington’s disease individuals for clinical trials.</p></div><div><h3>Methods</h3><p>We used data from 451 gene positive individuals with Huntington’s disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement.</p></div><div><h3>Results</h3><p>The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm³/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %).</p></div><div><h3>Conclusions</h3><p>This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.</p></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213158224000895/pdfft?md5=ea85763ab1db332826b98149f36f92ac&pid=1-s2.0-S2213158224000895-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141978909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal analysis of disease onset in Alzheimer’s disease using Connectome, Molecular, and genetics data","authors":"Sewook Oh ,&nbsp;Sunghun Kim ,&nbsp;Jong-eun Lee ,&nbsp;Bo-yong Park ,&nbsp;Ji Hye Won ,&nbsp;Hyunjin Park","doi":"10.1016/j.nicl.2024.103660","DOIUrl":"10.1016/j.nicl.2024.103660","url":null,"abstract":"<div><p>Alzheimer’s disease (AD) and its related age at onset (AAO) are highly heterogeneous, due to the inherent complexity of the disease. They are affected by multiple factors, such as neuroimaging and genetic predisposition. Multimodal integration of various data types is necessary; however, it has been nontrivial due to the high dimensionality of each modality. We aimed to identify multimodal biomarkers of AAO in AD using an extended version of sparse canonical correlation analysis, in which we integrated two imaging modalities, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), and genetic data in the form of single-nucleotide polymorphisms (SNPs) obtained from the Alzheimer’s disease neuroimaging initiative database. These three modalities cover low-to-high-level complementary information and offer multiscale insights into the AAO. We identified multivariate markers of AAO in AD using fMRI, PET, and SNP. Furthermore, the markers identified were largely consistent with those reported in the existing literature. In particular, our serial mediation analysis suggests that genetic variants influence the AAO in AD by indirectly affecting brain connectivity by mediation of amyloid-beta protein accumulation, supporting a plausible path in existing research. Our approach provides comprehensive biomarkers related to AAO in AD and offers novel multimodal insights into AD.</p></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213158224000998/pdfft?md5=14a71181e22529140c4fb03a2150908a&pid=1-s2.0-S2213158224000998-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142083915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VASARI-auto: Equitable, efficient, and economical featurisation of glioma MRI","authors":"James K. Ruffle ,&nbsp;Samia Mohinta ,&nbsp;Kelly Pegoretti Baruteau ,&nbsp;Rebekah Rajiah ,&nbsp;Faith Lee ,&nbsp;Sebastian Brandner ,&nbsp;Parashkev Nachev ,&nbsp;Harpreet Hyare","doi":"10.1016/j.nicl.2024.103668","DOIUrl":"10.1016/j.nicl.2024.103668","url":null,"abstract":"<div><p>The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used clinically. We sought to resolve this problem with software automation and machine learning. Using glioma data from 1172 patients, we developed VASARI-auto, an automated labelling software applied to open-source lesion masks and an openly available tumour segmentation model. Consultant neuroradiologists independently quantified VASARI features in 100 held-out glioblastoma cases. We quantified 1) agreement across neuroradiologists and VASARI-auto, 2) software equity, 3) an economic workforce analysis, and 4) fidelity in predicting survival. Tumour segmentation was compatible with the current state of the art and equally performant regardless of age or sex. A modest inter-rater variability between in-house neuroradiologists was comparable to between neuroradiologists and VASARI-auto, with far higher agreement between VASARI-auto methods. The time for neuroradiologists to derive VASARI was substantially higher than VASARI-auto (mean time per case 317 vs. 3 s). A UK hospital workforce analysis forecast that three years of VASARI featurisation would demand 29,777 consultant neuroradiologist workforce hours and &gt;£1.5 ($1.9) million, reducible to 332 hours of computing time (and £146 of power) with VASARI-auto. The best-performing survival model utilised VASARI-auto features instead of those derived by neuroradiologists. VASARI-auto is a highly efficient and equitable automated labelling system, a favourable economic profile if used as a decision support tool, and non-inferior survival prediction. Future work should iterate upon and integrate such tools to enhance patient care.</p></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213158224001074/pdfft?md5=b46614e6b4c1744e0fca04eac0c7273b&pid=1-s2.0-S2213158224001074-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study","authors":"Naici Liu ,&nbsp;Rebekka Lencer ,&nbsp;Christina Andreou ,&nbsp;Mihai Avram ,&nbsp;Heinz Handels ,&nbsp;Wenjing Zhang ,&nbsp;Sun Hui ,&nbsp;Chengmin Yang ,&nbsp;Stefan Borgwardt ,&nbsp;John A. Sweeney ,&nbsp;Su Lui ,&nbsp;Alexandra I. Korda","doi":"10.1016/j.nicl.2024.103686","DOIUrl":"10.1016/j.nicl.2024.103686","url":null,"abstract":"<div><h3>Background</h3><div>Measures of cortical topology are believed to characterize large-scale cortical networks. Previous studies used region of interest (ROI)-based approaches with predefined templates that limit analyses to linear pair-wise interactions between regions. As cortical topology is inherently complex, a non-linear dynamic model that measures the brain complexity at the voxel level is suggested to characterize topological complexities of brain regions and cortical folding.</div></div><div><h3>Methods</h3><div>T1-weighted brain images of 150 first-episode antipsychotic-naïve schizophrenia (FES) patients and 161 healthy comparison participants (HC) were examined. The Chaos analysis approach was applied to detect alterations in brain structural complexity using the largest Lyapunov exponent (Lambda) as the key measure. Then, the Lambda spatial series was mapped in the frequency domain using the correlation of the Morlet wavelet to reflect cortical folding complexity.</div></div><div><h3>Results</h3><div>A widespread voxel-wise decrease in Lambda values in space and frequency domains was observed in FES, especially in frontal, parietal, temporal, limbic, basal ganglia, thalamic, and cerebellar regions. The widespread decrease indicates a general loss of brain topological complexity and cortical folding. An additional pattern of increased Lambda values in certain regions highlights the redistribution of complexity measures in schizophrenia at an early stage with potential progression as the illness advances. Strong correlations were found between the duration of untreated psychosis and Lambda values related to the cerebellum, temporal, and occipital gyri.</div></div><div><h3>Conclusions</h3><div>Our findings support the notion that defining brain complexity by non-linear dynamic analyses offers a novel approach for identifying structural brain alterations related to the early stages of schizophrenia.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-modal MRI for objective diagnosis and outcome prediction in depression","authors":"Jesper Pilmeyer ,&nbsp;Rolf Lamerichs ,&nbsp;Sjir Schielen ,&nbsp;Faroeq Ramsaransing ,&nbsp;Vivianne van Kranen-Mastenbroek ,&nbsp;Jacobus F.A. Jansen ,&nbsp;Marcel Breeuwer ,&nbsp;Svitlana Zinger","doi":"10.1016/j.nicl.2024.103682","DOIUrl":"10.1016/j.nicl.2024.103682","url":null,"abstract":"<div><h3>Research Purpose</h3><div>The low treatment effectiveness in major depressive disorder (MDD) may be caused by the subjectiveness in clinical examination and the lack of quantitative tests. Objective biomarkers derived from magnetic resonance imaging (MRI) may support clinical experts during decision-making. Numerous studies have attempted to identify such MRI-based biomarkers. However, the majority is uni-modal (based on a single MRI modality) and focus on either MDD diagnosis or outcome. Uncertainty remains regarding whether key features or classification models for diagnosis may also be used for outcome prediction. Therefore, we aim to find multi-modal predictors of both, MDD diagnosis and outcome. By addressing these research questions using the same dataset, we eliminate between-study confounding factors.</div><div>Various structural (T₁-weighted, T₂-weighted, diffusion tensor imaging (DTI)) and functional (resting-state and task-based functional MRI) scans were acquired from 32 MDD and 31 healthy control (HC) subjects during the first visit at the investigational site (baseline). Depression severity was assessed at baseline and 6 months later. Features were extracted from the baseline MRI images with different modalities. Binary 6-months negative and positive outcome (NO; PO) classes were defined based on relative (to baseline) change in depression severity. Support vector machine models were employed to separate MDD from HC (diagnosis) and NO from PO subjects (outcome). Classification was performed through a uni-modal (features from a single MRI modality) and multi-modal (combination of features from different modalities) approach.</div></div><div><h3>Principal Results</h3><div>Our results show that DTI features yielded the highest uni-modal performance for diagnosis and outcome prediction: mean diffusivity (AUC (area under the curve) = 0.701) and the sum of streamline weights (AUC = 0.860), respectively. Multi-modal ensemble classifiers with T₁-weighted, resting-state functional MRI and DTI features improved classification performance for both diagnosis and outcome (AUC = 0.746 and 0.932, respectively). Feature analyses revealed that the most important features were located in frontal, limbic and parietal areas. However, the modality or location of these features was different between diagnostic and prognostic models.</div></div><div><h3>Major Conclusions</h3><div>Our findings suggest that combining features from different MRI modalities predict MDD diagnosis and outcome with higher performance. Furthermore, we demonstrated that the most important features for MDD diagnosis were different and located in other brain regions than those for outcome. This longitudinal study contributes to the identification of objective biomarkers of MDD and its outcome. Follow-up studies may further evaluate the generalizability of our models in larger or multi-center cohorts.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reorganization of integration and segregation networks in brain-based visual impairment","authors":"Ibai Diez ,&nbsp;Carla Troyas ,&nbsp;Corinna M. Bauer ,&nbsp;Jorge Sepulcre ,&nbsp;Lotfi B. Merabet","doi":"10.1016/j.nicl.2024.103688","DOIUrl":"10.1016/j.nicl.2024.103688","url":null,"abstract":"<div><div>Growing evidence suggests that cerebral connectivity changes its network organization by altering modular topology in response to developmental and environmental experience. However, changes in cerebral connectivity associated with visual impairment due to early neurological injury are still not fully understood. Cerebral visual impairment (CVI) is a brain-based visual disorder associated with damage and maldevelopment of retrochiasmal pathways and areas implicated in visual processing. In this study, we used a multimodal imaging approach and connectomic analyses based on structural (voxel-based morphometry; VBM) and resting state functional connectivity (rsfc) to investigate differences in weighted degree and link-level connectivity in individuals with CVI compared to controls with neurotypical development. We found that participants with CVI showed significantly reduced grey matter volume within the primary visual cortex and intraparietal sulcus (IPS) compared to controls. Participants with CVI also exhibited marked reorganization characterized by increased integration of visual connectivity to somatosensory and multimodal integration areas (dorsal and ventral attention regions) and lower connectivity from visual to limbic and default mode networks. Link-level functional changes in CVI were also associated with key clinical outcomes related to visual function and development. These findings provide early insight into how visual impairment related to early brain injury distinctly reorganizes the functional network architecture of the human brain.</div></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142480788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional magnetic resonance imaging alternations in suicide attempts individuals and their association with gene expression","authors":"Yurong Jiang ,&nbsp;Yujing Zhou ,&nbsp;Yingying Xie ,&nbsp;Junzi Zhou ,&nbsp;Mengjing Cai ,&nbsp;Jie Tang ,&nbsp;Feng Liu ,&nbsp;Juanwei Ma ,&nbsp;Huaigui Liu","doi":"10.1016/j.nicl.2024.103645","DOIUrl":"10.1016/j.nicl.2024.103645","url":null,"abstract":"<div><h3>Background</h3><p>Functional Magnetic Resonance Imaging (fMRI) has shown brain activity alterations in individuals with a history of attempted suicide (SA) who are diagnosed with depression disorder (DD) or bipolar disorder (BD). However, patterns of spontaneous brain activity and their genetic correlations need further investigation.</p></div><div><h3>Methods</h3><p>A voxel-based meta-analysis of 19 studies including 26 datasets, involving 742 patients with a history of SA and 978 controls (both nonsuicidal patients and healthy controls) was conducted. We examined fMRI changes in SA patients and analyzed the association between these changes and gene expression profiles using data from the Allen Human Brain Atlas by partial least squares regression analysis.</p></div><div><h3>Results</h3><p>SA patients demonstrated increased spontaneous brain activity in several brain regions including the bilateral inferior temporal gyrus, hippocampus, fusiform gyrus, and right insula, and decreased activity in areas like the bilateral paracentral lobule and inferior frontal gyrus. Additionally, 5,077 genes were identified, exhibiting expression patterns associated with SA-related fMRI alterations. Functional enrichment analyses demonstrated that these SA-related genes were enriched for biological functions including glutamatergic synapse and mitochondrial structure. Concurrently, specific expression analyses showed that these genes were specifically expressed in the brain tissue, in neurons cells, and during early developmental periods.</p></div><div><h3>Conclusion</h3><p>Our findings suggest a neurobiological basis for fMRI abnormalities in SA patients with DD or BD, potentially guiding future genetic and therapeutic research.</p></div>","PeriodicalId":54359,"journal":{"name":"Neuroimage-Clinical","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213158224000846/pdfft?md5=56e252b53890b72e28938425d4c6c178&pid=1-s2.0-S2213158224000846-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141768078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}