Autism spectrum disorder and attention-deficit/hyperactivity disorder (ADHD) are associated with complex changes as revealed by functional magnetic resonance imaging. To date, neuroimaging-based ...models are not able to characterize individuals with sufficient sensitivity and specificity. Further, although evidence shows that ADHD traits occur in individuals with autism spectrum disorder, and autism spectrum disorder traits in individuals with ADHD, the neurofunctional basis of the overlap is undefined.
Using individuals from the Autism Brain Imaging Data Exchange and ADHD-200, we apply a data-driven, subject-level approach, connectome-based predictive modeling, to resting-state functional magnetic resonance imaging data to identify brain–behavior associations that are predictive of symptom severity. We examine cross-diagnostic commonalities and differences.
Using leave-one-subject-out and split-half analyses, we define networks that predict Social Responsiveness Scale, Autism Diagnostic Observation Schedule, and ADHD Rating Scale scores and confirm that these networks generalize to novel subjects. Networks share minimal overlap of edges (<2%) but some common regions of high hubness (Brodmann areas 10, 11, and 21, cerebellum, and thalamus). Further, predicted Social Responsiveness Scale scores for individuals with ADHD are linked to ADHD symptoms, supporting the hypothesis that brain organization relevant to autism spectrum disorder severity shares a component associated with attention in ADHD. Predictive connections and high-hubness regions are found within a wide range of brain areas and across conventional networks.
An individual's functional connectivity profile contains information that supports dimensional, nonbinary classification in autism spectrum disorder and ADHD. Furthermore, we can determine disorder-specific and shared neurofunctional pathology using our method.
Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or ...distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.
Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural ...activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employ wide-field Ca
imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determine cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks exhibit overlapping organization. We find that there is considerable network overlap (both modalities) in addition to disjoint organization. Our results show that multiple BOLD networks are detected via Ca
signals, and networks determined by low-frequency Ca
signals are only modestly more similar to BOLD networks. In addition, the principal gradient of functional connectivity is nearly identical for BOLD and Ca
signals. Despite similarities, important differences are also detected across modalities, such as in measures of functional connectivity strength and diversity. In conclusion, Ca
imaging uncovers overlapping functional cortical organization in the mouse that reflects several, but not all, properties observed with fMRI-BOLD signals.
Functional magnetic resonance imaging in chronic ischaemic stroke Lake, Evelyn M. R.; Bazzigaluppi, Paolo; Stefanovic, Bojana
Philosophical transactions of the Royal Society of London. Series B. Biological sciences,
10/2016, Letnik:
371, Številka:
1705
Journal Article
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Ischaemic stroke is the leading cause of adult disability worldwide. Effective rehabilitation is hindered by uncertainty surrounding the underlying mechanisms that govern long-term ischaemic injury ...progression. Despite its potential as a sensitive non-invasive in vivo marker of brain function that may aid in the development of new treatments, blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has found limited application in the clinical research on chronic stage stroke progression. Stroke affects each of the physiological parameters underlying the BOLD contrast, markedly complicating the interpretation of BOLD fMRI data. This review summarizes current progress on application of BOLD fMRI in the chronic stage of ischaemic injury progression and discusses means by which more information may be gained from such BOLD fMRI measurements. Concomitant measurements of vascular reactivity, neuronal activity and metabolism in preclinical models of stroke are reviewed along with illustrative examples of post-ischaemic evolution in neuronal, glial and vascular function. The realization of the BOLD fMRI potential to propel stroke research is predicated on the carefully designed preclinical research establishing an ischaemia-specific quantitative model of BOLD signal contrast to provide the framework for interpretation of fMRI findings in clinical populations.
This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.
To aid in development of chronic stage treatments for sensorimotor deficits induced by ischemic stroke, we investigated the effects of GABA antagonism on brain structure and fine skilled reaching in ...a rat model of focal ischemia induced via cortical microinjections of endothelin-1 (ET-1). Beginning 7 days after stroke, animals were administered a gamma-aminobutyric acid (GABAA) inverse agonist, L-655,708, at a dose low enough to afford α5-GABAA receptor specificity. A week after stroke, the ischemic lesion comprised a small hypointense necrotic core (6 ± 1 mm3) surrounded by a large (62 ± 11 mm3) hyperintense perilesional region; the skilled reaching ability on the Montoya staircase test was decreased to 34% ± 2% of the animals' prestroke performance level. On L-655,708 treatment, animals showed a progressive decrease in total stroke volume (13 ± 4 mm3 per week), with no change in animals receiving placebo. Concomitantly, treated animals' skilled reaching progressively improved by 9% ± 1% per week, so that after 2 weeks of treatment, these animals performed at 65% ± 6% of their baseline ability, which was 25% ± 11% better than animals given placebo. These data indicate beneficial effects of delayed, sustained low-dose GABAA antagonism on neuroanatomic injury and skilled reaching in the chronic stage of stroke recovery in an ET-1 rat model of focal ischemia.
Where do we stand on fMRI in awake mice? Mandino, Francesca; Vujic, Stella; Grandjean, Joanes ...
Cerebral cortex (New York, N.Y. 1991),
01/2024, Letnik:
34, Številka:
1
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Imaging awake animals is quickly gaining traction in neuroscience as it offers a means to eliminate the confounding effects of anesthesia, difficulties of inter-species translation (when humans are ...typically imaged while awake), and the inability to investigate the full range of brain and behavioral states in unconscious animals. In this systematic review, we focus on the development of awake mouse blood oxygen level dependent functional magnetic resonance imaging (fMRI). Mice are widely used in research due to their fast-breeding cycle, genetic malleability, and low cost. Functional MRI yields whole-brain coverage and can be performed on both humans and animal models making it an ideal modality for comparing study findings across species. We provide an analysis of 30 articles (years 2011-2022) identified through a systematic literature search. Our conclusions include that head-posts are favorable, acclimation training for 10-14 d is likely ample under certain conditions, stress has been poorly characterized, and more standardization is needed to accelerate progress. For context, an overview of awake rat fMRI studies is also included. We make recommendations that will benefit a wide range of neuroscience applications.
Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches ...that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.
•10 simple rules to help researchers apply predictive modeling to connectivity data.•Rules are general to methodological approach with practical examples.•4 rules for validating predictive models through independent data.•3 rules for assessing model performance.•3 rules for removing confounds and increasing interpretability of models.
It is a longstanding goal of neuroimaging to produce reliable, generalizable models of brain behavior relationships. More recently, data driven predictive models have become popular. However, ...overfitting is a common problem with statistical models, which impedes model generalization. Cross validation (CV) is often used to estimate expected model performance within sample. Yet, the best way to generate brain behavior models, and apply them out-of-sample, on an unseen dataset, is unclear. As a solution, this study proposes an ensemble learning method, in this case resample aggregating, encompassing both model parameter estimation and feature selection. Here we investigate the use of resampled aggregated models when used to estimate fluid intelligence (fIQ) from fMRI based functional connectivity (FC) data. We take advantage of two large openly available datasets, the Human Connectome Project (HCP), and the Philadelphia Neurodevelopmental Cohort (PNC). We generate aggregated and non-aggregated models of fIQ in the HCP, using the Connectome Prediction Modelling (CPM) framework. Over various test-train splits, these models are evaluated in sample, on left-out HCP data, and out-of-sample, on PNC data. We find that a resample aggregated model performs best both within- and out-of-sample. We also find that feature selection can vary substantially within-sample. More robust feature selection methods, as detailed here, are needed to improve cross sample performance of CPM based brain behavior models.
Brain activity during consciousness has a unique spatiotemporal signature that is evolutionarily conserved. A recent study uses functional magnetic resonance imaging to show how spontaneous activity ...in the murine brain reconfigures with wakefulness.
Brain activity during consciousness has a unique spatiotemporal signature that is evolutionarily conserved. A recent study uses functional magnetic resonance imaging to show how spontaneous activity in the murine brain reconfigures with wakefulness.
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