Abstract Introduction We performed a systematic review and meta-analysis of the Alzheimer's disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and ...mild cognitive impairment, using resting-state functional magnetic resonance imaging. Methods Studies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies. Results Thirty-four studies were included (1363 participants, average 40 per study). Consistent alterations in connectivity were found in the default mode, salience, and limbic networks in patients with AD dementia, mild cognitive impairment, or in both groups. We also identified a strong tendency in the literature toward specific examination of the default mode network. Discussion Convergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that highly connected hub regions in the brain might be an early target of AD.
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 3T 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 Cohen's 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.
•Small 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.
This study aimed to investigate, through functional MRI (fMRI), the neuronal substrates associated with the consolidation process of two motor skills: motor sequence learning (MSL) and motor ...adaptation (MA). Four groups of young healthy individuals were assigned to either (i) a night/sleep condition, in which they were scanned while practicing a finger sequence learning task or an eight-target adaptation pointing task in the evening (test) and were scanned again 12 h later in the morning (retest) or (ii) a day/awake condition, in which they were scanned on the MSL or the MA tasks in the morning and were rescanned 12 h later in the evening. As expected and consistent with the behavioral results, the functional data revealed increased test-retest changes of activity in the striatum for the night/sleep group compared with the day/awake group in the MSL task. By contrast, the results of the MA task did not show any difference in test-retest activity between the night/sleep and day/awake groups. When the two MA task groups were combined, however, increased test-retest activity was found in lobule VI of the cerebellar cortex. Together, these findings highlight the presence of both functional and structural dissociations reflecting the off-line consolidation processes of MSL and MA. They suggest that MSL consolidation is sleep dependent and reflected by a differential increase of neural activity within the corticostriatal system, whereas MA consolidation necessitates either a period of daytime or sleep and is associated with increased neuronal activity within the corticocerebellar system.
16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Deficit-Hyperactivity-Disorder (ADHD), but their impact on ...functional connectivity (FC) remains unclear. Here we report an analysis of resting-state FC using magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We characterize CNV FC-signatures and use them to identify dimensions contributing to complex idiopathic conditions. CNVs have large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions play a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibit worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms.
Estimated years to symptom onset (EYO) is a measurement developed to track disease progression in patients with genetic Alzheimer's disease. Vogel et al. apply this concept to individuals with a ...parental history of sporadic Alzheimer's disease. A neuroimaging-based model successfully predicts EYO and years to clinical conversion in unseen data.
See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.
Abstract
See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.
Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.
Abstract 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.
Even though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. ...Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.
Our understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes this neurodevelopmental disorder. Data driven clustering ...offers a straightforward way to decompose autism heterogeneity into subtypes of connectivity and promises an unbiased framework to investigate behavioral symptoms and causative genetic factors. Yet, the robustness and generalizability of functional connectivity subtypes is unknown. Here, we show that a simple hierarchical cluster analysis can robustly relate a given individual and brain network to a connectivity subtype, but that continuous assignments are more robust than discrete ones. We also found that functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and these associations generalize to independent replication data. We explored systematically 18 different brain networks as we expected them to associate with different behavioral profiles as well as different key regions. Contrary to this prediction, autism functional connectivity subtypes converged on a common topography across different networks, consistent with a compression of the primary gradient of functional brain organization, as previously reported in the literature. Our results support the use of data driven clustering as a reliable data dimensionality reduction technique, where any given dimension only associates moderately with clinical manifestations.
The ‘learning and performance’ conundrum has for a long time puzzled the field of cognitive neuroscience. Deciphering the genuine functional neuroanatomy of motor sequence learning, among that of ...other skills, has thereby been hampered. The main caveat is that changes in neural activity that inherently accompany task practice may not only reflect the learning process per se, but also the basic motor implementation of improved performance. Previous research has attempted to control for a performance confound in brain activity by adopting methodologies that prevent changes in performance. However, blocking the expression of performance is likely to distort the very nature of the motor sequence learning process, and may thus represent a major confound in itself. In the present study, we postulated that both learning-dependent plasticity mechanisms and learning-independent implementation processes are nested within the relationship that exists between performance and brain activity. Functional magnetic resonance imaging (fMRI) was used to map brain responses in healthy volunteers while they either (a) learned a novel sequence, (b) produced a highly automatized sequence or (c) executed non-sequential movements matched for speed frequency. In order to dissociate between qualitatively distinct, but intertwined, relationships between performance and neural activity, our analyses focused on correlations between variations in performance and brain activity, and how this relationship differs or shares commonalities between conditions. Results revealed that activity in the putamen and contralateral lobule VI of the cerebellum most strongly correlated with performance during learning per se, suggesting their key role in this process. By contrast, activity in a parallel cerebellar network, as well as in motor and premotor cortical areas, was modulated by performance during learning and during one or both control condition(s), suggesting the primary contribution of these areas in motor implementation, either as a function or not of the sequential content of movements. Our findings thus highlight the multifaceted nature of the link between performance and brain activity, and suggest that different components of the striato-cortical and cerebello-cortical motor loops play distinct, but complementary, roles during early motor sequence learning.
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