Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of C9orf72.
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ABSTRACT:
SUBMITTER: Bruffaerts R
PROVIDER: S-EPMC9311825 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Frontotemporal dementia (FTD) is a neurodegenerative disease with a strong genetic basis. Understanding the structural brain changes during pre-symptomatic stages may allow for earlier diagnosis of patients suffering from FTD; therefore, we investigated asymptomatic members of FTD families with mutations in C9orf72 and granulin (GRN) genes. Clinically asymptomatic subjects from families with C9orf72 mutation (15 mutation carriers, C9orf72+; and 23 non-carriers, C9orf72-) and GRN mutations (9 mutation carriers, GRN+; and 15 non-carriers, GRN-) underwent structural neuroimaging (MRI). Cortical thickness and subcortical gray matter volumes were calculated using FreeSurfer. Group differences were evaluated, correcting for age, sex and years to mean age of disease onset within the subject's family. Mean age of C9orf72+ and C9orf72- were 42.6?±?11.3 and 49.7?±?15.5?years, respectively; while GRN+ and GRN- groups were 50.1?±?8.7 and 53.2?±?11.2?years respectively. The C9orf72+ group exhibited cortical thinning in the temporal, parietal and frontal regions, as well as reduced volumes of bilateral thalamus and left caudate compared to the entire group of mutation non-carriers (NC: C9orf72- and GRN- combined). In contrast, the GRN+ group did not show any significant differences compared to NC. C9orf72 mutation carriers demonstrate a pattern of reduced gray matter on MRI prior to symptom onset compared to GRN mutation carriers. These findings suggest that the preclinical course of FTD differs depending on the genetic basis and that the choice of neuroimaging biomarkers for FTD may need to take into account the specific genes involved in causing the disease.
| S-EPMC5964622 | biostudies-literature
Project description:BackgroundUnsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization.ResultsWe introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy.ConclusionsWe demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward's clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images.
| S-EPMC4225570 | biostudies-literature
Project description:Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns.
| S-EPMC3470591 | biostudies-literature
Project description:Previous cross-sectional imaging studies found differences in brain structure and in resting state networks between presymptomatic carriers of mutations in C9orf72 (C9+) and healthy controls. We carried out a prospective longitudinal study of clinical and resting state functional imaging in a cohort of 15 presymptomatic C9+ carriers to determine whether differences in resting state connectivity prior to developing symptoms reflect static developmental differences or ongoing low-grade degenerative changes. Presymptomatic C9+ carriers were scanned at baseline with follow-up scanning at 6- and 18-months and compared to a cohort of 14 healthy controls scanned longitudinally. Resting state networks associated with manifest disease were visualized by comparing 27 symptomatic C9+ carriers to 34 healthy controls. Motor, salience, thalamic, and speech production networks were visualized using a seed-based analysis. Neurofilament light chain was measured in serum obtained at the time of the scans. Neither clinical measures of motor, cognitive, and behavioral function nor neurofilament levels changed over follow-up in presymptomatic C9+ carriers. In thalamic networks, there was a reduction in connectivity in presymptomatic carriers at all timepoints with a constant difference compared to healthy controls. In contrast, precuneus/posterior cingulate regions exhibited declining functional connectivity compared to controls over the 18-month follow-up, particularly in motor networks. These were regions that also exhibited reduced functional connectivity in symptomatic C9+ carriers. Reduced connectivity over time also occurred in small regions of frontal and temporal cortex within salience and thalamic networks in presymptomatic C9+ carriers. A few areas of increased connectivity occurred, including cortex near the motor seed and within the speech production network. Overall, changes in functional connectivity over time favor the explanation of ongoing low-grade alterations in presymptomatic C9+ carriers in most networks, with the exception of thalamic networks where functional connectivity reductions were stable over time. The loss of connectivity to parietal cortex regions in several different networks may be a distinct feature of C9orf72-related degeneration. Longitudinal studies of carriers who phenoconvert will be important to determine the prognostic significance of presymptomatic functional connectivity alterations.
| S-EPMC7406915 | biostudies-literature
Project description:BackgroundThe characterisation of presymptomatic disease-burden patterns in asymptomatic mutation carriers has a dual academic and clinical relevance. The understanding of disease propagation mechanisms is of considerable conceptual interests, and defining the optimal time of pharmacological intervention is essential for improved clinical trial outcomes.MethodsIn a prospective, multimodal neuroimaging study, 22 asymptomatic C9orf72 GGGGCC hexanucleotide repeat carriers, 13 asymptomatic subjects with SOD1, and 54 "gene-negative" ALS kindreds were enrolled. Cortical and subcortical grey matter alterations were systematically appraised using volumetric, morphometric, vertex, and cortical thickness analyses. Using a Bayesian approach, the thalamus and amygdala were further parcellated into specific nuclei and the hippocampus was segmented into anatomically defined subfields.ResultsAsymptomatic GGGGCC hexanucleotide repeat carriers in C9orf72 exhibited early subcortical changes with the preferential involvement of the pulvinar and mediodorsal regions of the thalamus, as well as the lateral aspect of the hippocampus. Volumetric approaches, morphometric methods, and vertex analyses were anatomically consistent in capturing focal subcortical changes in asymptomatic C9orf72 hexanucleotide repeat expansion carriers. SOD1 mutation carriers did not exhibit significant subcortical grey matter alterations. In our study, none of the two asymptomatic cohorts exhibited cortical grey matter alterations on either cortical thickness or morphometric analyses.DiscussionThe presymptomatic radiological signature of C9orf72 is associated with selective thalamic and focal hippocampal degeneration which may be readily detectable before cortical grey matter changes ensue. Our findings confirm selective subcortical grey matter involvement early in the course of C9orf72-associated neurodegeneration.
| S-EPMC10421803 | biostudies-literature
Project description:BackgroundBrain parcellation is important for exploiting neuroimaging data. Variability in physiology between individuals has led to the need for data-driven approaches to parcellation, with recent research focusing on simultaneously estimating and partitioning the network structure of the brain.New methodWe view data preprocessing, parcellation, and parcel validation from the perspective of predictive modeling. The goal is to identify parcels in a way that best generalizes to unseen data. We utilize an uncertainty quantification approach from image science to define parcels as groups of unresolvable variables in the predictive model. Model parameters are chosen via cross-validation. Parcellation results are compared based on both their repeatability as well as their ability to describe held-out data.ResultsThe approach provides insight and strategies for open questions such as the choice of evaluation metrics, selection of model order, and the optimal tuning of preprocessing steps for functional imaging data.Comparison with existing methodsWe compare new and established approaches using functional imaging data, where we find the proposed approach produces parcellations which are both more accurate and more repeatable than the current baseline clustering method. The metrics also demonstrate potential problems with overfitting for the baseline method, and with bias for other methods.ConclusionsClustering of resolution provides a principled and robust approach to brain parcellation which improves on the current baseline.
| S-EPMC7061089 | biostudies-literature
Project description:ImportancePresymptomatic carriers of chromosome 9 open reading frame 72 (C9orf72) mutation, the most frequent genetic cause of frontotemporal lobar degeneration and amyotrophic lateral sclerosis, represent the optimal target population for the development of disease-modifying drugs. Preclinical biomarkers are needed to monitor the effect of therapeutic interventions in this population.ObjectivesTo assess the occurrence of cognitive, structural, and microstructural changes in presymptomatic C9orf72 carriers.Design, setting, and participantsThe PREV-DEMALS study is a prospective, multicenter, observational study of first-degree relatives of individuals carrying the C9orf72 mutation. Eighty-four participants entered the study between October 2015 and April 2017; 80 (95%) were included in cross-sectional analyses of baseline data. All participants underwent neuropsychological testing and magnetic resonance imaging; 63 (79%) underwent diffusion tensor magnetic resonance imaging. Gray matter volumes and diffusion tensor imaging metrics were calculated within regions of interest. Anatomical and microstructural differences between individuals who carried the C9orf72 mutation (C9+) and those who did not carry the C9orf72 mutation (C9-) were assessed using linear mixed-effects models. Data were analyzed from October 2015 to April 2017.Main outcomes and measuresDifferences in neuropsychological scores, gray matter volume, and white matter integrity between C9+ and C9- individuals.ResultsOf the 80 included participants, there were 41 C9+ individuals (24 [59%] female; mean [SD] age, 39.8 [11.1] years) and 39 C9- individuals (24 [62%] female; mean [SD] age, 45.2 [13.9] years). Compared with C9- individuals, C9+ individuals had lower mean (SD) praxis scores (163.4 [6.1] vs 165.3 [5.9]; P = .01) and intransitive gesture scores (34.9 [1.6] vs 35.7 [1.5]; P = .004), atrophy in 8 cortical regions of interest and in the right thalamus, and white matter alterations in 8 tracts. When restricting the analyses to participants younger than 40 years, compared with C9- individuals, C9+ individuals had lower praxis scores and intransitive gesture scores, atrophy in 4 cortical regions of interest and in the right thalamus, and white matter alterations in 2 tracts.Conclusions and relevanceCognitive, structural, and microstructural alterations are detectable in young C9+ individuals. Early and subtle praxis alterations, underpinned by focal atrophy of the left supramarginal gyrus, may represent an early and nonevolving phenotype related to neurodevelopmental effects of C9orf72 mutation. White matter alterations reflect the future phenotype of frontotemporal lobar degeneration/amyotrophic lateral sclerosis, while atrophy appears more diffuse. Our results contribute to a better understanding of the preclinical phase of C9orf72 disease and of the respective contribution of magnetic resonance biomarkers.Trial registrationclinicaltrials.gov Identifier: NCT02590276.
| S-EPMC5838615 | biostudies-literature
Project description:Hierarchical clustering of pancreatic cancer cell lines based on differentially regulated genes between mesenchymal and epithelial PDAC cells derived from primary tumours and metastases from KrasG12D-driven mouse models of pancreatic cancer.
2022-11-19 | E-MTAB-8173 | biostudies-arrayexpress
Project description:Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/.
| S-EPMC3838923 | biostudies-literature
Project description:Brain size variation over primate evolution and human development is associated with shifts in the proportions of different brain regions. Individual brain size can vary almost twofold among typically developing humans, but the consequences of this for brain organization remain poorly understood. Using in vivo neuroimaging data from more than 3000 individuals, we find that larger human brains show greater areal expansion in distributed frontoparietal cortical networks and related subcortical regions than in limbic, sensory, and motor systems. This areal redistribution recapitulates cortical remodeling across evolution, manifests by early childhood in humans, and is linked to multiple markers of heightened metabolic cost and neuronal connectivity. Thus, human brain shape is systematically coupled to naturally occurring variations in brain size through a scaling map that integrates spatiotemporally diverse aspects of neurobiology.
| S-EPMC7485526 | biostudies-literature