Christian Dansereau

Impact of the resolution of brain parcels on connectome-wide association studies in fMRI.

A recent trend in functional magnetic resonance imaging is to test for association of clinical disorders with every possible connection between selected brain parcels. We investigated the impact of the resolution of functional brain parcels, ranging from large-scale networks to local regions, on a mass univariate general linear model (GLM) of connectomes. For each resolution taken independently, the Benjamini-Hochberg procedure controlled the false-discovery rate (FDR) at nominal level on realistic simulations. However, the FDR for tests pooled across all resolutions could be inflated compared to the FDR within resolution. This inflation was severe in the presence of no or weak effects, but became negligible for strong effects. We thus developed an omnibus test to establish the overall presence of true discoveries across all resolutions. Although not a guarantee to control the FDR across resolutions, the omnibus test may be used for descriptive analysis of the impact of resolution on a GLM analysis, in complement to a primary analysis at a predefined single resolution. On three real datasets with significant omnibus test (schizophrenia, congenital blindness, motor practice), markedly higher rate of discovery were obtained at low resolutions, below 50, in line with simulations showing increase in sensitivity at such resolutions. This increase in discovery rate came at the cost of a lower ability to localize effects, as low resolution parcels merged many different brain regions together. However, with 30 or more parcels, the statistical effect maps were biologically plausible and very consistent across resolutions. These results show that resolution is a key parameter for GLM-connectome analysis with FDR control, and that a functional brain parcellation with 30 to 50 parcels may lead to an accurate summary of full connectome effects with good sensitivity in many situations.

Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis

We performed a systematic review and meta‐analysis of the Alzheimers disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment, using resting‐state functional magnetic resonance imaging.

Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies.

Resting-state functional connectivity is a promising biomarker for Alzheimers disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimers disease and amnestic mild cognitive impairment (aMCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from aMCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore combined four datasets collected independently, including 112 healthy controls and 143 patients with aMCI. We systematically tested multiple brain connections for associations with aMCI using a weighted average routinely used in meta-analyses. The largest effects involved the superior medial frontal cortex (including the anterior cingulate), dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with aMCI exhibited significantly decreased connectivity between default mode network nodes and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified common aMCI-related connectivity changes with small to medium effect sizes and sample size estimates recommending a minimum of 140 to upwards of 600 total subjects to achieve adequate statistical power in the context of a multisite study with 5–10 scanning sites and about 10 subjects per group and per site. If our findings can be replicated and associated with other established biomarkers of Alzheimers disease (e.g., amyloid and tau quantification), then these functional connections may be promising candidate biomarkers for Alzheimers disease.

Statistical power and prediction accuracy in multisite resting-state fMRI connectivity

ABSTRACT Connectivity studies using resting‐state functional magnetic resonance imaging are increasingly pooling data acquired at multiple sites. While this may allow investigators to speed up recruitment or increase sample size, multisite studies also potentially introduce systematic biases in connectivity measures across sites. In this work, we measure the inter‐site effect in connectivity and its impact on our ability to detect individual and group differences. Our study was based on real, as opposed to simulated, multisite fMRI datasets collected in N=345 young, healthy subjects across 8 scanning sites with 3 T scanners and heterogeneous scanning protocols, drawn from the 1000 functional connectome project. We first empirically show that typical functional networks were reliably found at the group level in all sites, and that the amplitude of the inter‐site effects was small to moderate, with a Cohens effect size below 0.5 on average across brain connections. We then implemented a series of Monte‐Carlo simulations, based on real data, to evaluate the impact of the multisite effects on detection power in statistical tests comparing two groups (with and without the effect) using a general linear model, as well as on the prediction of group labels with a support‐vector machine. As a reference, we also implemented the same simulations with fMRI data collected at a single site using an identical sample size. Simulations revealed that using data from heterogeneous sites only slightly decreased our ability to detect changes compared to a monosite study with the GLM, and had a greater impact on prediction accuracy. However, the deleterious effect of multisite data pooling tended to decrease as the total sample size increased, to a point where differences between monosite and multisite simulations were small with N=120 subjects. Taken together, our results support the feasibility of multisite studies in rs‐fMRI provided the sample size is large enough. HighlightsSmall to moderate systematic site effects in fMRI connectivity.Small impact of site effects on the detection of group differences for sample size >100.Linear regression of the sites prior to multivariate prediction do not improve prediction accuracy.

State-dependent modulation of functional connectivity in early blind individuals

ABSTRACT Resting‐state functional connectivity (RSFC) studies have provided strong evidences that visual deprivation influences the brains functional architecture. In particular, reduced RSFC coupling between occipital (visual) and temporal (auditory) regions has been reliably observed in early blind individuals (EB) at rest. In contrast, task‐dependent activation studies have repeatedly demonstrated enhanced co‐activation and connectivity of occipital and temporal regions during auditory processing in EB. To investigate this apparent discrepancy, the functional coupling between temporal and occipital networks at rest was directly compared to that of an auditory task in both EB and sighted controls (SC). Functional brain clusters shared across groups and cognitive states (rest and auditory task) were defined. In EBs, we observed higher occipito‐temporal correlations in activity during the task than at rest. The reverse pattern was observed in SC. We also observed higher temporal variability of occipito‐temporal RSFC in EB suggesting that occipital regions in this population may play the role of a multiple demand system. Our study reveals how the connectivity profile of sighted and early blind people is differentially influenced by their cognitive state, bridging the gap between previous task‐dependent and RSFC studies. Our results also highlight how inferring group‐differences in functional brain architecture solely based on resting‐state acquisition has to be considered with caution. HIGHLIGHTSTemporo‐occipital functional connectivity is modified by cognitive states.This modulation is different in blind and sighted individuals.Blind participants have higher temporo‐occipital temporal variability at rest.The group difference in variability at rest explains the differences in modulation.Inferring group differences with resting‐state data should be subject to caution.

Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity

Our objective was to assess the generalizability, across sites and cognitive contexts, of schizophrenia classification based on functional brain connectivity. We tested different training-test scenarios combining fMRI data from 191 schizophrenia patients and 191 matched healthy controls obtained at 6 scanning sites and under different task conditions. Diagnosis classification accuracy generalized well to a novel site and cognitive context provided data from multiple sites were used for classifier training. By contrast, lower classification accuracy was achieved when data from a single distinct site was used for training. These findings indicate that it is beneficial to use multisite data to train fMRI-based classifiers intended for large-scale use in the clinical realm.

A power analysis for multisite studies in resting-state functional connectivity, with an application to clinical trials in Alzheimer's disease

pial towhite matter surfaces were computed across the entire cerebral cortex using an automated image processing method. An intensity profile was generated along each streamline for b-amyloid, GFAP, and NeuN qIHC volumes, and these profiles were averaged over neuroanatomical regions-ofinterest. Comparisons were performed between Tg and WT groups. Results:Distinct cortical laminar profiles were observed for b-amyloid and astrocytes in the Tg mice. While heavy bands of astrogliosis were typically found in superficial and deep cortex, b-amyloid deposits were most abundant in the mid-cortical layers. We also identified previously unreported differences in the regional, multi-parametric, cortical laminar profiles between Tg andWTmice.Conclusions:Our robust, fully-automated method for extraction of cortical laminar profiles of multiple, co-registered IHC markers across the entire mouse cerebral cortex revealed unique observations regarding the patterns of AD-associated alterations in APP Tg mice. We are currently generating similar laminar profiles of vascular and synaptic density in order to obtain a comprehensive picture of neuronal, glial, and vascular pathological changes. Future studies will interrogate the relationships between these cellular measures and in vivo structural and functional imaging data, which will improve our understanding of the complex pathophysiological process underlying AD.

Subtypes of functional brain connectivity as early markers of neurodegeneration in Alzheimer's disease

Highlights Reliable functional brain network subtypes accompany cognitive impairment in AD Symptom-related subtypes exist in the default-mode, limbic and salience networks A limbic subtype is associated with a familial risk of AD in healthy older adults Limbic subtypes also associate with beta amyloid deposition and ApoE4 In Brief We found reliable subtypes of functional brain connectivity networks in older adults, associated with AD-related clinical symptoms in patients as well as several AD risk factors/biomarkers in asymptomatic individuals. Summary The heterogeneity of brain degeneration has not been investigated yet for functional brain network connectivity, a promising biomarker of Alzheimer’s disease. We coupled cluster analysis with resting-state functional magnetic resonance imaging to discover connectivity subtypes in healthy older adults and patients with cognitive disorders related to Alzheimer’s disease, noting associations between subtypes and cognitive symptoms in the default-mode, limbic and salience networks. In an independent asymptomatic cohort with a family history of Alzheimer’s dementia, the connectivity subtypes had good test-retest reliability across all tested networks. We found that a limbic subtype was overrepresented in these individuals, which was previously associated with symptoms. Other limbic subtypes showed associations with cerebrospinal fluid Aβ1-42 levels and ApoE4 genotype. Our results demonstrate the existence of reliable subtypes of functional brain networks in older adults and support future investigations in limbic connectivity subtypes as early biomarkers of Alzheimer’s degeneration.

A dataset of multiresolution functional brain parcellations in an elderly population with no or mild cognitive impairment

We present group eight resolutions of brain parcellations for clusters generated from resting-state functional magnetic resonance images for 99 cognitively normal elderly persons and 129 patients with mild cognitive impairment, pooled from four independent datasets. This dataset was generated as part of the following study: Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies (Tam et al., 2015) [1]. The brain parcellations have been registered to both symmetric and asymmetric MNI brain templates and generated using a method called bootstrap analysis of stable clusters (BASC) (Bellec et al., 2010) [2]. We present two variants of these parcellations. One variant contains bihemisphereic parcels (4, 6, 12, 22, 33, 65, 111, and 208 total parcels across eight resolutions). The second variant contains spatially connected regions of interest (ROIs) that span only one hemisphere (10, 17, 30, 51, 77, 199, and 322 total ROIs across eight resolutions). We also present maps illustrating functional connectivity differences between patients and controls for four regions of interest (striatum, dorsal prefrontal cortex, middle temporal lobe, and medial frontal cortex). The brain parcels and associated statistical maps have been publicly released as 3D volumes, available in .mnc and .nii file formats on figshare and on Neurovault. Finally, the code used to generate this dataset is available on Github.

Consistent inter-protocol differences in resting-state functional connectomes between normal aging and mild cognitive impairment

Resting-state functional connectivity is a promising biomarker for Alzheimer’s disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimer’s disease and mild cognitive impairment (MCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from MCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore pooled four independent datasets, including 112 healthy controls and 143 patients with MCI, systematically testing multiple brain connections for consistent differences. The largest effects associated with MCI involved the ventromedial and dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with MCI exhibited significantly decreased connectivity within the frontal lobe, between frontal and temporal areas, and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified robust MCI-related connectivity changes that appear to be promising candidate biomarkers for Alzheimer’s disease.