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  • 1
    In: Human Brain Mapping, Wiley, Vol. 40, No. 13 ( 2019-09), p. 3982-4000
    Abstract: Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross‐sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under‐sampling and measurement errors and should be able to integrate multi‐modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi‐task learning based approach to modeling univariate trajectories across subjects that addresses these criteria. Our approach learns multiple subjects' trajectories within a single model that allows for different types of information sharing, that is, coupling , across subjects. It optimizes a combination of uncoupled, fully coupled and kernel coupled models. Kernel‐based coupling allows linking subjects' trajectories based on one or more biomarker measures. We demonstrate this using Alzheimer's Disease Neuroimaging Initiative (ADNI) data, where we model longitudinal trajectories of MRI‐derived cortical volumes in neurodegeneration, with coupling based on APOE genotype, cerebrospinal fluid (CSF) and amyloid PET‐based biomarkers. In addition to detecting established disease effects, we detect disease related changes within the insula that have not received much attention within the literature. Due to its sensitivity in detecting disease effects, its competitive predictive performance and its ability to learn the optimal parameter covariance from data rather than choosing a specific set of random and fixed effects a priori , we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. A software implementation of the method is publicly available.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1492703-2
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  • 2
    In: Human Brain Mapping, Wiley, Vol. 42, No. 8 ( 2021-06), p. 2546-2555
    Abstract: Identifying brain processes involved in the risk and development of mental disorders is a major aim. We recently reported substantial interindividual heterogeneity in brain structural aberrations among patients with schizophrenia and bipolar disorder. Estimating the normative range of voxel‐based morphometry (VBM) data among healthy individuals using a Gaussian process regression (GPR) enables us to map individual deviations from the healthy range in unseen datasets. Here, we aim to replicate our previous results in two independent samples of patients with schizophrenia ( n 1 = 94; n 2 = 105), bipolar disorder ( n 1 = 116; n 2 = 61), and healthy individuals ( n 1 = 400; n 2 = 312). In line with previous findings with exception of the cerebellum our results revealed robust group level differences between patients and healthy individuals, yet only a small proportion of patients with schizophrenia or bipolar disorder exhibited extreme negative deviations from normality in the same brain regions. These direct replications support that group level‐differences in brain structure disguise considerable individual differences in brain aberrations, with important implications for the interpretation and generalization of group‐level brain imaging findings to the individual with a mental disorder.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 1492703-2
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  • 3
    In: Human Brain Mapping, Wiley, Vol. 43, No. 5 ( 2022-04), p. 1749-1765
    Abstract: Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T 1 ‐weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T 1 ‐FLAIR, T 2 , T 2 *, T 2 ‐FLAIR, DWI and ADC contrasts, acquired in ~1 min), as well as to slower, more standard single‐contrast T 1 ‐weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix T 1 ‐FLAIR and single‐contrast T 1 ‐weighted scans, using correlations between voxels and regions of interest across participants, measures of within‐ and between‐participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix‐derived data using test–retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
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  • 4
    In: Human Brain Mapping, Wiley, Vol. 34, No. 5 ( 2013-05), p. 1102-1114
    Abstract: Pattern recognition approaches to the analysis of neuroimaging data have brought new applications such as the classification of patients and healthy controls within reach. In our view, the reliance on expensive neuroimaging techniques which are not well tolerated by many patient groups and the inability of most current biomarker algorithms to accommodate information about prior class frequencies (such as a disorder's prevalence in the general population) are key factors limiting practical application. To overcome both limitations, we propose a probabilistic pattern recognition approach based on cheap and easy‐to‐use multi‐channel near‐infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls ( n = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high‐accuracy single subject classification of patients with schizophrenia ( n = 40) and healthy controls ( n = 40) is possible based on temporal patterns of fNIRS data measured during a working memory task. For classification, we integrate spatial and temporal information at each channel to estimate overall classification accuracy. This yields an overall accuracy of 76% which is comparable to the highest ever achieved in biomarker‐based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub‐second, multivariate temporal patterns of BOLD responses and high‐accuracy predictions based on low‐cost, easy‐to‐use fNIRS patterns. In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2013
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  • 5
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S6 ( 2022-12)
    Abstract: Patterns of atrophy in the brain highly differ between individual Alzheimer’s Disease (AD) patients, however, most clinical research focuses on group‐level differences (Verdi et al., 2021). Here we used a novel technique, neuroanatomical normative modelling, to reveal individual patterns of cortical thickness by quantifying deviations from the normative range. We applied a hierarchical Bayesian regression (HBR) normative model (Kia et al., 2021), to amyloid‐positive AD patients who had clinical amyloid PET imaging at the Imperial Memory Clinic (IMC). Patients had either AD dementia or mild cognitive impairment due to AD. We hypothesised that there would be individual differences in patterns of cortical atrophy in these patients. Method Cortical thickness across 148 regions of interest (ROIs) was generated using FreeSurfer from n=130 (102 AD patients and 28 healthy controls) T1‐weighted MRIs acquired from the IMC. A reference HBR normative model was trained on a separate dataset of n=34,490 healthy individuals to index population variability, which predicted cortical thickness at each ROI using age and sex. This generated cortical thickness z‐scores for each ROI, per patient (z‐score 〈 ‐1.96 = outlier). Result The patterns of cortical atrophy outliers were highly varied in amyloid‐positive AD patients. For instance, the largest proportion of outliers in a region was 60% within the superior temporal sulci, if atrophy were homogenous we would expect 100% of outliers to be here (Fig.1) . This heterogeneity was also seen when comparing how similar outlier patterns were between patients (Fig.2) . We also found that the proportions of outliers differ according to disease severity, e.g. the highest percentage of outliers was 70% within superior temporal sulci within the AD dementia subgroup, and 50% in superior temporal sulci in the mild cognitive impairment due to AD subgroup (Fig.3) . Conclusion Amyloid‐positivity results in heterogenous patterns of cortical atrophy. This is more pronounced in AD dementia, though still present in people with mild cognitive impairment due to AD. Neuroanatomical normative maps have the potential to be individualised markers of disease, and with application to longitudinal data could track individual disease progression.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2201940-6
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  • 6
    In: Bipolar Disorders, Wiley, Vol. 18, No. 7 ( 2016-11), p. 612-623
    Abstract: Recent studies have indicated that pattern recognition techniques of functional magnetic resonance imaging ( fMRI ) data for individual classification may be valuable for distinguishing between major depressive disorder ( MDD ) and bipolar disorder ( BD ). Importantly, medication may have affected previous classification results as subjects with MDD and BD use different classes of medication. Furthermore, almost all studies have investigated only depressed subjects. Therefore, we focused on medication‐free subjects. We additionally investigated whether classification would be mood state independent by including depressed and remitted subjects alike. Methods We applied Gaussian process classifiers to investigate the discriminatory power of structural MRI (gray matter volumes of emotion regulation areas) and resting‐state fMRI (resting‐state networks implicated in mood disorders: default mode network [ DMN ], salience network [ SN ], and lateralized frontoparietal networks [ FPNs ]) in depressed (n=42) and remitted (n=49) medication‐free subjects with MDD and BD . Results Depressed subjects with MDD and BD could be classified based on the gray matter volumes of emotion regulation areas as well as DMN functional connectivity with 69.1% prediction accuracy. Prediction accuracy using the FPN s and SN did not exceed chance level. It was not possible to discriminate between remitted subjects with MDD and BD . Conclusions For the first time, we showed that medication‐free subjects with MDD and BD can be differentiated based on structural MRI as well as resting‐state functional connectivity. Importantly, the results indicated that research concerning diagnostic neuroimaging tools distinguishing between MDD and BD should consider mood state as only depressed subjects with MDD and BD could be correctly classified. Future studies, in larger samples are needed to investigate whether the results can be generalized to medication‐naïve or first‐episode subjects.
    Type of Medium: Online Resource
    ISSN: 1398-5647 , 1399-5618
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 2001157-X
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  • 7
    In: Bipolar Disorders, Wiley, Vol. 14, No. 4 ( 2012-06), p. 451-460
    Type of Medium: Online Resource
    ISSN: 1398-5647 , 1399-5618
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2012
    detail.hit.zdb_id: 2001157-X
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  • 8
    In: Alzheimer's & Dementia, Wiley, Vol. 17, No. S4 ( 2021-12)
    Abstract: Alzheimer’s Disease (AD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology. Neuroimaging biomarkers have considerable utility in AD research, however, common statistical designs do not capture neuroanatomical heterogeneity, generally assuming the effects of AD on the brain will be the same in different patients. Spatial normative modelling is an emerging technique that can reveal individual patterns of neuroanatomy by quantifying deviations from normative ranges (Verdi et al., 2021). On multi‐site Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied a hierarchical Bayesian regression (HBR) spatial normative model (Kia et al., 2020). We compared patterns of cortical thickness heterogeneity in AD patients, people with Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) people. Method Structural imaging data provided estimates of cortical thickness across 148 (ROIs), generated using FreeSurfer on n=563 T1‐weighted MRI scans acquired across 38 sites. We then applied the HBR spatial normative model, using a separate healthy reference dataset of n=33,073 to index population variability, predicting cortical thickness at each ROI using age and sex. Next, we applied transfer learning, to recalibrate the normative model to the CN participants from ADNI. This generated cortical thickness z‐scores across ROIs for each participant (Fig. 1). Z‐scores 〈 ‐1.96 were identified as outliers. Result Linear regression revealed group differences of outliers summed across 148 ROIs: AD had significantly more ROI outliers than MCI and CN participants (β=10.99,95%CI=[5.55,16.42], p=8.28×10 ‐5 ) (Fig. 2). ANOVAs at each ROI demonstrated that this difference was within temporal regions (Fig. 3) , e.g., parahippocampal gyrus (F(2,23.33),CI=[ 0.29, 0.57],FDR p=3.60×10 ‐8 ). Calculating the percentage of outliers in each ROI within groups revealed that outlying ROIs in AD patients only partially overlap. The parahippocampal gyrus had the highest number of AD patients with outliers (n=27 out of n=59 (45.7%)); more than half of the AD group appeared normal in this region (Fig. 4). Conclusion Our novel quantitative estimate of spatial heterogeneity of cortical thickness in AD patients suggests that the impact of AD on the brain is not consistent between patients. Individualised patient neuroanatomical maps have the potential to be a marker of disease, and could be used to track an individual’s disease progression or treatment response.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2201940-6
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  • 9
    In: Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, Wiley, Vol. 16, No. 1 ( 2024-01)
    Abstract: Overlooking the heterogeneity in Alzheimer's disease (AD) may lead to diagnostic delays and failures. Neuroanatomical normative modeling captures individual brain variation and may inform our understanding of individual differences in AD‐related atrophy. METHODS We applied neuroanatomical normative modeling to magnetic resonance imaging from a real‐world clinical cohort with confirmed AD ( n  = 86). Regional cortical thickness was compared to a healthy reference cohort ( n  = 33,072) and the number of outlying regions was summed (total outlier count) and mapped at individual‐ and group‐levels. RESULTS The superior temporal sulcus contained the highest proportion of outliers (60%). Elsewhere, overlap between patient atrophy patterns was low. Mean total outlier count was higher in patients who were non‐amnestic, at more advanced disease stages, and without depressive symptoms. Amyloid burden was negatively associated with outlier count. DISCUSSION Brain atrophy in AD is highly heterogeneous and neuroanatomical normative modeling can be used to explore anatomo‐clinical correlations in individual patients.
    Type of Medium: Online Resource
    ISSN: 2352-8729 , 2352-8729
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2024
    detail.hit.zdb_id: 2832898-X
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  • 10
    In: Human Brain Mapping, Wiley, Vol. 35, No. 7 ( 2014-07), p. 3083-3094
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 1492703-2
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