Biology subjects

Duchesne, S.

Publications and source records attributed to Duchesne, S..

16 recordsLinked to original sources

bioRxiv2025

Resting-state functional MRI derivatives: A dataset derived from the The Comprehensive Assessment of Neurodegeneration and Dementia Study

Resting-state functional connectivity (RSFC) holds promise for the detection and characterisation of dementia. The Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) Study, by the Canadian Consortium on Neurodegeneration in Aging (CCNA), provides a unique resource to study deeply phenotyped neurodegenerative conditions. We present RSFC derivatives for 784 participants (data release 7 of the cohort) who were either cognitively unimpaired or diagnosed primarily with Alzheimers disease (AD), mixed dementia (AD with a vascular component), mild cognitive impairment (MCI), vascular MCI, frontotemporal dementia, Parkinsons disease with or without MCI or dementia, Lewy body disease or subjective cognitive impairment. Functional MRI scans were preprocessed using fMRIPrep, and time-series and whole-brain connectomes generated using three atlases at multiple resolutions, denoised using seven different techniques. High-motion artifacts were managed using a liberal quality control threshold appropriate for an older clinical population, resulting in data from 680 participants. These derivatives are made available to the research community to accelerate research on RSFC biomarkers of neurodegenerative disease, reducing duplication of effort, saving computational resources, and improving standardisation across studies.

neuroscience↗

bioRxiv2024

Automatization and validation of the hippocampal-to-ventricle ratio in a clinical sample

BackgroundThe hippocampal-to-ventricle ratio (HVR) is a biomarker of medial temporal atrophy, particularly useful in the assessment of neurodegeneration in diseases such as Alzheimers disease (AD). To minimize subjectivity and inter-rater variability, an automated, accurate, precise, and reliable segmentation technique for the hippocampus (HC) and surrounding cerebro-spinal fluid (CSF) filled spaces -- such as the temporal horns of the lateral ventricles -- is essential. MethodsWe trained and evaluated three automated methods for the segmentation of both HC and CSF (Multi-Atlas Label Fusion (MALF), Nonlinear Patch-Based Segmentation (NLPB), and a Convolutional Neural Network (CNN)). We then evaluated these methods, including the widely used FreeSurfer technique, using baseline T1w MRIs of 1,641 participants from the AD Neuroimaging Initiative study with various degree of atrophy associated with their cognitive status on the spectrum from cognitively healthy to clinically probable AD. Our gold standard consisted in manual segmentation of HC and CSF from 80 cognitively healthy individuals. We calculated HC volumes and HVR and compared all methods in terms of segmentation reliability, similarity across methods, sensitivity in detecting between-group differences and associations with age, scores of the learning subtest of the Rey Auditory Verbal Learning Test (RAVLT) and the Alzheimers Disease Assessment Scale 13 (ADAS13) scores. ResultsCross validation demonstrated that the CNN method yielded more accurate HC and CSF segmentations when compared to MALF and NLPB, demonstrating higher volumetric overlap (Dice Kappa = 0.94) and correlation (rho = 0.99) with the manual labels. It was also the most reliable method in clinical data application, showing minimal failures. Our comparisons yielded high correlations between FreeSurfer, CNN and NLPB volumetric values. HVR yielded higher control:AD effect sizes than HC volumes among all segmentation methods, reinforcing the significance of HVR in clinical distinction. AssociationsThe positive association with age was significantly stronger for HVR compared to HC volumes on all methods except FreeSurfer. Memory associations with HC volumes or HVR were only significant for individuals with mild cognitive impairment. Finally, the HC volumes and HVR showed comparable negative associations with ADAS13, particularly in the mild cognitive impairment cohort. ConclusionThis study provides an evaluation of automated segmentation methods centered to estimate HVR, emphasizing the superior performance of a CNN-based algorithm. The findings underscore the pivotal role of accurate segmentation in HVR calculations for precise clinical applications, contributing valuable insights into medial temporal lobe atrophy in neurodegenerative disorders, especially AD. AuthorshipO_LISofia Fernandez-Lozano: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft, Visualization. C_LIO_LIVladimir Fonov: Software, Data Curation. C_LIO_LIDorothee Schoemaker: Resources, Writing - Review & Editing. C_LIO_LIJens Pruessner: Resources, Writing - Review & Editing. C_LIO_LIOlivier Potvin: Resources, Writing - Review & Editing. C_LIO_LISimon Duchesne: Resources, Writing - Review & Editing. C_LIO_LID. Louis Collins: Conceptualization, Writing - Review & Editing, Supervision. C_LI

neuroscience↗

bioRxiv2022

A Scoping Review Of Mathematical Models Covering Alzheimer's Disease Progression

Alzheimers disease is a complex, multi-factorial and multi-parametric neurodegenerative etiology. Mathematical models can help understand such a complex problem by providing a way to explore and conceptualize principles, merging biological knowledge with experimental data into a model amenable to simulation and external validation, all without the need for extensive clinical trials. We performed a scoping review of mathematical models of AD with a search strategy applied to the PubMed database which yielded 846 entries. After applying our exclusion criteria, only 17 studies remained from which we extracted data, focusing on three aspects of mathematical modeling: how authors addressed continuous time, how models were solved, and how the high dimensionality and non-linearity of models were managed. Most articles modeled AD at the cellular range of the disease process, operating on a short time scale (e.g., minutes; hours), i.e., the micro view (12/17); the rest considered regional or brain-level processes, with longer timescales (e.g., years, decades) (the macro view). Most papers were concerned primarily with A{beta} (n = 8), few modeled with both A{beta} and tau proteins (n = 3), and some considered more than these two factors in the model (n = 6). Models used partial differential equations (PDEs; n = 3), ordinary differential equations (ODEs; n = 7), both PDEs and ODEs (n = 3). Some didnt specify the mathematical formalism (n = 4). Sensitivity analyses were performed in only a small number of papers (4/17). Overall, we found that only two studies could be considered valid in terms of parameters and conclusions, and two more were partially valid. The majority (n = 13) either was invalid or there was insufficient information to ascertain their status. While mathematical models are powerful and useful tools for the study of AD, closer attention to reporting is necessary to gauge the quality of published studies to replicate or continue with their contributions.

neuroscience↗

bioRxiv2022

Using Transfer Learning for Automated Microbleed Segmentation

IntroductionCerebral microbleeds are small perivascular haemorrhages that can occur in both grey and white matter brain regions. Microbleeds are a marker of cerebrovascular pathology, and are associated with an increased risk of cognitive decline and dementia. Microbleeds can be identified and manually segmented by expert radiologists and neurologists, usually from susceptibility-contrast MRI. The latter is hard to harmonize across scanners, while manual segmentation is laborious, time-consuming, and subject to inter- and intra-rater variabiltiy. Automated techniques so far have shown high accuracy at a neighborhood ("patch") level at the expense of a high number of false positives voxel-wise lesions. We aimed to develop an automated, more precise microbleeds segmentation tool able to use standardizable MRI contrasts. MethodsWe first trained a ResNet50 network on another MRI segmentations task (cerberospinal fluid versus background segmentation) using T1-weighted, T2-weighted, and T2* MRI. We then used transfer learning to train the network for the detection of microbleeds with the same contrasts. As a final step, we employed a combination of morphological operators and rules at the local lesion level to remove false positives. Manual segmentations of microbleeds from 78 participants were used to train and validate the system. We assessed the impact of patch size, freezing weights of the initial layers, mini-batch size, learning rate, as well as data augmentation on the performance of the Microbleed ResNet50 network. ResultsThe proposed method achieved a high performance, with a patch-level sensitivity, specificity, and accuracy of 99.57%, 99.16%, and 99.93%, respectively. At a per lesion level, sensitivity, precision, and Dice similarity index values were 89.1%, 20.1%, and 0.28 for cortical GM; 100%, 100%, and 1.0 for deep GM; and 91.1%, 44.3%, and 0.58 for WM, respectively. DiscussionThe proposed microbleed segmentation method is more suitable for the automated detection of microbleeds with high sensitivity.

neuroscience↗

bioRxiv2021

White Matter Hyperintensity Distribution Differences in Aging and Neurodegenerative Disease Cohorts

IntroductionWhite matter hyperintensities (WMHs) are common magnetic resonance imaging (MRI) findings in the aging population in general, as well as in patients with neurodegenerative diseases. They are known to exacerbate the cognitive deficits and worsen the clinical outcomes in the patients. However, it is not well-understood whether there are disease-specific differences in prevalence and distribution of WMHs in different neurodegenerative disorders. MethodsData included 976 participants with cross-sectional T1-weighted and fluid attenuated inversion recovery (FLAIR) MRIs from the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) cohort of the Canadian Consortium on Neurodegeneration in Aging (CCNA) with eleven distinct diagnostic groups: cognitively intact elderly (CIE), subjective cognitive impairment (SCI), mild cognitive impairment (MCI), vascular MCI (V-MCI), Alzheimers dementia (AD), vascular AD (V-AD), frontotemporal dementia (FTD), Lewy body dementia (LBD), cognitively intact elderly with Parkinsons disease (PD-CIE), cognitively impaired Parkinsons disease (PD-CI), and mixed dementias. WMHs were segmented using a previously validated automated technique. WMH volumes in each lobe and hemisphere were compared against matched CIE individuals, as well as each other, and between men and women. ResultsAll cognitively impaired diagnostic groups had significantly greater overall WMH volumes than the CIE group. Vascular groups (i.e. V-MCI, V-AD, and mixed dementia) had significantly greater WMH volumes than all other groups, except for FTD, which also had significantly greater WMH volumes than all non-vascular groups. Women tended to have lower WMH burden than men in most groups and regions, controlling for age. The left frontal lobe tended to have a lower WMH burden than the right in all groups. In contrast, the right occipital lobe tended to have greater WMH loads than the left. ConclusionsThere were distinct differences in WMH prevalence and distribution across diagnostic groups, sexes, and in terms of asymmetry. WMH burden was significantly greater in all neurodegenerative dementia groups, likely encompassing areas exclusively impacted by neurodegeneration as well as areas related to cerebrovascular disease pathology.

neuroscience↗

bioRxiv2021

The last battle of Anne of Brittany: solving mass grave through an interdisciplinary approach (paleopathology, anthropobiology, history, multiple isotopes and radiocarbon dating)

Mass graves are usually key historical markers with strong incentive for archeological investigations. The identification of individuals buried in mass graves has long benefitted from traditional historical, archaeological, anthropological and paleopathological techniques. The addition of novel methods including genetic, genomic and isotopic geochemistry have renewed interest in solving unidentified mass graves. In this study, we demonstrate that the combined use of these techniques allows the identification of the individuals found in two Breton historical mass graves, where one method alone would not have revealed the importance of this discovery. The skeletons likely belong to soldiers from the two enemy armies who fought during a major event of Breton history: the siege of Rennes in 1491, which ended by the wedding of the Duchess of Brittany with the King of France and signaled the end of the independence of the region. Our study highlights the value of interdisciplinary approaches with a particular emphasis on increasingly accurate isotopic markers. The development of the sulfur isoscape and testing of the triple isotope geographic assignment are detailed in a companion paper [1].

ecology↗

bioRxiv2021

NOMIS: Quantifying morphometric deviation from normality over the lifetime in the adult human brain

We present NOMIS (https://github.com/medicslab/NOMIS), a comprehensive open MRI tool to assess morphometric deviation from normality in the adult human brain. Based on MR anatomical images from 6,909 cognitively healthy individuals aged 18-100 years, we modeled 1,344 measures computed using the open access FreeSurfer pipeline, considering account personal characteristics (age, sex, intracranial volume) and image quality (resolution, contrast-to-noise ratio and surface reconstruction defect holes), and providing expected values for any new individual. Then, for each measure, the NOMIS tool was built to generate Z-score effect sizes denoting the extent of deviation from the normative sample. Depending on the user need, NOMIS offers four versions of Z-score adjusted on different sets of variables. While all versions consider head size and image quality, they can also incorporate age and/or sex, thereby facilitating multi-site neuromorphometric research across adulthood.

neuroscience↗

bioRxiv2020

Amyloid β Impacts Future Freezing of Gait in Parkinson's Disease Via White Matter Hyperintensities

BackgroundFreezing of gait (FOG) is a common symptom in Parkinsons Disease (PD) patients. Previous studies have reported relationships between FOG, substantia nigra (SN) degeneration, dopamine transporter (DAT) concentration, as well as amyloid {beta} deposition. However, there is a paucity of research on the concurrent impact of white matter damage. ObjectivesTo assess the inter-relationships between these different co-morbidities, their impact on future FOG and whether they act independently of each other. MethodsWe used baseline MRI and longitudinal gait data from the Parkinsons Progression Markers Initiative (PPMI). We used deformation based morphometry (DBM) from T1-weighted MRI to measure SN atrophy, and segmentation of white matter hyperintensities (WMH) as a measure of WM pathological load. Putamen and caudate DAT levels from SPECT as well as cerebrospinal fluid (CSF) amyloid {beta} were obtained directly from the PPMI. Following correlation analyses, we investigated whether WMH burden mediates the impact of amyloid {beta} on future FOG. ResultsSN DBM, WMH load, putamen and caudate DAT activity and CSF amyloid {beta} levels were significantly different between PD patients with and without future FOG (p < 0.008). Mediation analysis demonstrated an effect of CSF amyloid {beta} levels on future FOG via WMH load, independent of SN atrophy and striatal DAT activity levels. ConclusionsAmyloid {beta} might impact future FOG in PD patients through an increase in WMH burden, in a pathway independent of Lewy body pathology.

neuroscience↗

bioRxiv2020

Beware of White Matter Hyperintensities Causing Systematic Errors in Grey Matter Segmentations!

IntroductionVolumetric estimates of subcortical and cortical structures, extracted from T1-weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer grey matter (GM) structure volumes and its possible bias on functional relationships. MethodsT1-weighted images from 1077 participants (4321 timepoints) from the Alzheimers Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2-weighted or Fluid-attenuated inversion recovery (FLAIR) images. Mixed effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overal WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance. ResultsParticipants with higher WMH volumes had higher overalps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (P < 0.0001). When not corrected for WMHs, caudate volumes increased with age (P < 0.0001) and were not different between cognitively healthy individuals and age-matched probable Alzheimers disease patients. After correcting for WMHs, caudate volumes decreased with age (P < 0.0001), and Alzheimers disease patients had lower caudate volumes than cognitively healthy individuals (P < 0.01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (P < 0.0001). ConclusionsPresence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.

neuroscience↗

bioRxiv2020

The Temporal Relationships between White Matter Hyperintensities, Neurodegeneration, Amyloid β, and Cognition

INTRODUCTIONCognitive decline in Alzheimers disease is associated with amyloid-{beta} accumulation, neurodegeneration and cerebral small vessel disease, but the temporal relationships between these factors is not well established. METHODSData included white matter hyperintensity (WMH) load, grey matter (GM) atrophy and Alzheimers Disease Assessment Scale-Cognitive-Plus (ADAS13) scores for 720 participants and cerebrospinal fluid amyloid (A{beta}1-42) for 461 participants from the Alzheimers Disease Neuroimaging Initiative. Linear regressions were used to assess the relationships between baseline WMH, GM, and A{beta}1-42 to changes in WMH, GM, A{beta}1-42, and cognition at one-year follow-up. RESULTSBaseline WMHs and A{beta}1-42 predicted WMH increase and GM atrophy. Baseline WMHs, GM, and A{beta}1-42 predicted worsening cognition. Only baseline A{beta}1-42 predicted change in A{beta}1-42. DISCUSSIONBaseline WMHs lead to greater future GM atrophy and cognitive decline, suggesting that WM damage precedes neurodegeneration and cognitive decline. Baseline A{beta}1-42 predicted WMH increase, suggesting a potential role of amyloid in WM damage. Research in ContextO_LISystematic Review: Both amyloid {beta} and neurodegeneration are primary pathologies in Alzheimers disease. White matter hyperintensities (indicative of presence of cerebrovascular disease) might also be part of the pathological changes in Alzheimers. However, the temporal relationship between white matter hyperintensities, amyloid {beta}, neurodegeneration, and cognitive decline is still unclear. C_LIO_LIInterpretation: Our results establish a potential temporal order between white matter hyperintensities, amyloid {beta}, neurodegeneration, and cognitive decline, showing that white matter hyperintensities precede neurodegeneration and cognitive decline. The results provide some evidence that amyloid {beta} deposition, in turn, precedes accumulation of white matter hyperintensities. C_LIO_LIFuture Directions: The current findings reinforce the need for future longitudinal investigations of the mechanisms through which white matter hyperintensities impact the aging population in general and Alzheimers disease patients, in particular. C_LI

neuroscience↗

bioRxiv2020

Cognitive and Motor Correlates of Grey and White Matter Pathology in Parkinsons Disease

IntroductionPrevious studies have found associations between grey matter atrophy and white matter hyperintensities (WMH) of vascular origin with cognitive and motor deficits in Parkinsons disease (PD). Here we investigate these relationships in a sample of PD patients and age-matched healthy controls. MethodsData included 50 PD patients and 45 age-matched controls with T1-weighted and FLAIR scans at baseline, 18-months, and 36-months follow-up. Deformation-based morphometry was used to measure grey matter atrophy. SNIPE (Scoring by Nonlocal Image Patch Estimator) was used to measure Alzheimers disease-like textural patterns in the hippocampi. WMHs were segmented using T1-weighted and FLAIR images. The relationship between MRI features and clinical scores was assessed using mixed-effects models. The motor subscore of the Unified Parkinsons Disease Rating Scale (UPDRSIII), number of steps in a walking trial, and Dementia Rating Scale (DRS) were used respectively as measures of motor function, gait, and cognition. ResultsSubstantia nigra atrophy was significantly associated with motor deficits, with a greater impact in PDs (p<0.05). Hippocampal SNIPE scores were associated with cognitve decline in both PD and controls (p<0.01). WMH burden was significantly associated with cognitive decline and increased motor deficits in the PD group, and gait deficits in both PD and controls (p<0.03). ConclusionWhile substantia nigra atrophy and WMH burden were significantly associated with additional motor deficits, WMH burden and hippocampal atrophy were associated with cognitive deficits in PD patients. These results suggest an additive contribution of both grey and white matter damage to the motor and cognitive deficits in PD.

neuroscience↗

bioRxiv2020

Proof of concept of a novel ex vivo, in situ method for MRI and histological brain assessment

MRI-histology correlation studies of the ex vivo brain mostly employ fresh, extracted (ex situ) specimens, aldehyde fixed by immersion. This method entails manipulation of the fresh brain during extraction, introducing several disadvantages: deformation of the specimen prior to MRI acquisition; introduction of air bubbles in the sulci, creating artifacts; and uneven or poor fixation of the deeper regions of the brain. We propose a new paradigm to scan the ex vivo brain, exploiting a technique used by anatomists: fixation by whole body perfusion, which implies fixation of the brain in situ. This allows scanning the brain surrounded by fluids, meninges, and skull, thus preserving the structural relationships of the brain in vivo and avoiding the disadvantages of ex situ scanning. Our aims were: 1) to assess whether months of in situ fixation resulted in a loss of fluid around the brain; 2) to evaluate whether in situ fixation modified antigenicity for myelin and neuron specific marker; 3) to assess whether in situ fixation improved the register of ex vivo brain images to standard neuroanatomical templates in pseudo-Talairach space for morphometry studies. Five head specimens fixed with a saturated sodium chloride solution (a non-standard fixative used in our anatomy laboratory for neurosurgical simulation) were employed. We acquired 3D T1-weighted (MPRAGE), 2D fluid-attenuated inversion recovery T2-weighted turbo spin echo (T2w-FLAIR), and 3D gradient-echo (3D-GRE) pulse sequences of all brains on a 1.5T MRI. After brain extraction, sections were processed for binding with myelin basic protein (MBP) and neuronal nuclei (NeuN) primary antibodies by immunofluorescence. This study showed that all but one specimen retained fluids in the subarachnoid and ventricular spaces. The specimen that lost fluid was the oldest one, with the longest interval between the time of death and the MRI scanning day being 403 days. All T1-weighted images were successfully processed through a validated pipeline used with in vivo MRIs. The pipeline did not require any modification to run on the ex vivo-in situ scans. All scans were successfully registered to the brain template, more accurately than an ex vivo-ex situ scan and exhibited positive antigenicity for MBP and NeuN. MRI and histology study of the ex vivo-in situ brain fixed by perfusion is feasible and allows for in situ MRI imaging for of at least 10 months post-mortem prior to histology analyses. Fluids around and inside the brain specimens and antigenicity for myelin and neurons were all well preserved.

neuroscience↗

bioRxiv2020

Reliability Assessment of Tissue Classification Algorithms for Multi-Center and Multi-Scanner Data

BackgroundGray and white matter volume difference and change are important imaging markers of pathology and disease progression in neurology and psychiatry. Such measures are usually estimated from tissue segmentation maps produced by publicly available image processing pipelines. However, the reliability of the produced segmentations when using multi-center and multi-scanner data remains understudied. Here, we assess the robustness of six publicly available tissue classification pipelines across images acquired from different MR scanners and sites. MethodsWe used T1-weighted images of a single individual, scanned in 73 sessions across 27 different sites to assess the robustness of the tissue classification tools. Variability in Dice Kappa values and tissue volumes was assessed for Atropos, BISON, Classify_Clean, FAST, FreeSurfer, and SPM12. We also estimated the sample size necessary to detect a significant 1% volume reduction based on the variability of the estimates from each method within and across scanner models. ResultsBISON had the lowest overall variability in its volumetric estimates, followed by FreeSurfer, and SPM12. All methods also had significant differences between some of their estimates across different scanner manufacturers (e.g. BISON had significantly higher GM estimates and correspondingly lower WM estimates for GE scans compared to Philips and SIEMENs), and different signal-to-noise ratio (SNR) levels (e.g. FAST and FreeSurfer had significantly higher WM volume estimates for high versus medium and low SNR tertiles as well as correspondingly lower GM volume estimates). BISON also had the smallest sample size requirement across all scanners and tissue types, followed by FreeSurfer, and SPM12. ConclusionsOur comparisons provide a benchmark on the reliability of the publicly used tissue classification techniques and the amount of variability that can be expected when using large multi-center datasets and multi-scanner databases.

neuroscience↗

bioRxiv2019

SNIPE score can capture prodromal Alzheimer's in cognitively normal subjects

Capturing early changes in the brain related to Alzheimers disease may lead to models that successfully predict cognitive decline and the eventual onset of dementia, well ahead of onset of clinical symptoms. In this study we used both hippocampal volume and our hippocampal driven SNIPE score to show which marker better captures Alzheimers related changes in a large dataset of normal controls (N=515) from the ADNI study, comparing controls that remain cognitively stable and controls that progress to either MCI or Alzheimers dementia during 10 years of follow-up (median follow-up: 30 months). We measured hippocampal volume and SNIPE score and found that the effect size to differentiate between stable and progressor groups was significantly larger for SNIPE score than for volume. Our results also show that there is a significant age-related difference between groups for both markers, and the difference is greater with the SNIPE score. Our experiments show that considering high sensitivity of our SNIPE score regarding to early AD-related brain changes, this marker is a better candidate in comparison to hippocampal volume for predicting the future onset of dementia.

neuroscience↗

bioRxiv2018

Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors

Studies using resting-state functional magnetic resonance imaging (rsfMRI) are increasingly collecting data at multiple sites in order to speed up recruitment or increase sample size. The main objective of this study was to assess the long-term consistency of rsfMRI connectivity maps derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). Nine to ten minutes of functional BOLD images were acquired from an adult cognitively healthy volunteer scanned repeatedly at 13 Canadian sites on three scanner makes (General Electric, Philips and Siemens) over the course of 2.5 years. The consistency (spatial Pearsons correlation) of rsfMRI connectivity maps for seven canonical networks ranged from about 0.4-0.8 (intra-site) to 0.3-0.8 (inter-vendor), with a negligible effect of time. We noted systematic differences in data quality across vendors, which may also explain some of these results. We also pooled the long-term longitudinal data with a single-site, short-term (1 month) data sample acquired on 26 subjects (10 scans per subject), called HNU1. Using randomly selected pairs of scans from each subject, we quantified the ability of a data-driven unsupervised cluster analysis to match two scans of the same subjects. In this \"fingerprinting\" experiment, we found that scans from the Canadian subject (Csub) could be matched with high accuracy intra-site (>95% for some networks), but that the accuracy decreased substantially for scans drawn from different sites and vendors, while still remaining in the range of accuracies observed in HNU1. Overall, our results demonstrate good multivariate stability of rsfMRI measures over several years, but substantial impact of scanning site and vendors. How detrimental these effects are will depend on the application, yet improving methods for harmonizing multisite analysis is an important area for future work.\n\nHIGHLIGHTSO_LIConsistency of rsfMRI connectivity over 2.5 years, 13 sites and 3 scanner vendors\nC_LIO_LITime elapsed between scans had negligible effect on consistency\nC_LIO_LIConsistency decreased due to site and vendor differences\nC_LIO_LIAccuracy of connectivity fingerprints decreased due to site and vendor differences\nC_LI\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=181 HEIGHT=200 SRC=\"FIGDIR/small/497743v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (54K):\norg.highwire.dtl.DTLVardef@109bab7org.highwire.dtl.DTLVardef@1c1720corg.highwire.dtl.DTLVardef@12678e2org.highwire.dtl.DTLVardef@ca494d_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract\n\nC_FIG

neuroscience↗

bioRxiv2017

Relationships between clans and genetic kin explain cultural similarities over vast distances: the case of Yakutia

Archaeological studies sample ancient human populations one site at a time, often limited to a fraction of the regions and periods occupied by a given group. While this bias is known and discussed in the literature, few model populations span areas as large and unforgiving as the Yakuts of Eastern Siberia. We systematically surveyed 31,000 square kilometres in the Sakha Republic (Yakutia) and completed the archaeological study of 174 frozen graves, assembled between the 15th and the 19th century. We analysed genetic data (autosomal genotypes, Y-chromosome haplotypes and mitochondrial haplotypes) for all ancient subjects and confronted these to data on 190 modern subjects from the same area and the same population. Ancient familial links were identified between graves up to 1500 km apart, as well as paternal clans. We provide new insights on the origins of the contemporary Yakut population and demonstrate that cultural similarities in the past were linked to (i) the expansion of specific paternal clans, (ii) preferential marriage among the elites and (iii) funeral choices that could constitute a bias in any ancient population study.

genetics↗