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Introduction: Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies. Methods: In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets. Results: Conventional whitening methods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively. Discussion: This study highlights the urgent need to address the problem of serial correlation in short-TR (< 1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power.

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Alzheimer's disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta ( A ß ), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current A ß biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimer's process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for A ß detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF A ß status, and offer key insights into dynamic functional connectivity analysis in AD.

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INTRODUCTION: The study of Alzheimer's disease investigates topographic patterns of degeneration in the context of connected networks comprised of functionally distinct domains using increasingly sophisticated molecular techniques. Therefore, obtaining high precision and accuracy of neuropathologic tissue sampling will enhance the reliability of molecular studies and contribute to the understanding of Alzheimer's disease pathology. Neuroimaging tools can help assess these aspects of current sampling protocols as well as contribute directly to their improvement. METHODS: Using a virtual sampling method on magnetic resonance images (MRIs) from 35 participants (21 women), we compared the precision and accuracy of traditional neuropathologic vs. neuroimaging-guided sampling. The impact of the resulting differences was assessed by evaluating the functional connectivity pattern of regions selected by each approach. RESULTS: Virtual sampling using the traditional neuropathologic approach had low neuroanatomical precision and accuracy for all cortical regions tested. Neuroimaging-guided strategies narrowed these gaps. Discrepancies in the location of traditional and neuroimaging-guided samples corresponded to differences in fMRI measures of functional connectivity. DISCUSSION: Integrating neuroimaging tools with the neuropathologic assessment will improve neuropathologic-neuroimaging correlations by helping to ensure specific functional domains are accurately sampled for quantitative molecular neuropathologic applications. Our neuroimaging-based simulation of current sampling practices provides a benchmark of precision and accuracy against which to measure improvements when using novel tissue sampling approaches. Our results suggest that relying on gross landmarks alone to select samples at autopsy leads to significant variability, even when sampled by the same neuropathologist. Further, this exercise highlights how sampling precision could be enhanced if neuroimaging were integrated with the standard neuropathologic assessment. More accurate targeting and improved biological homogeneity of sampled brain tissue will facilitate the interpretation of neuropathological analyses in AD and the downstream research applications of brain tissue from biorepositories.

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While photons travel in a straight line at constant velocity in free space, the intensity profile of structured light may be tailored for acceleration in any degree of freedom. Here we propose a simple approach to control the angular acceleration of light. Using Laguerre-Gaussian modes as our twisted beams carrying orbital angular momentum, we show that superpositions of opposite handedness result in a radially dependent angular acceleration as they pass through a focus (waist plane). Due to conservation of orbital angular momentum, we find that propagation dynamics are complex despite the free-space medium: the outer part of the beam (rings) rotates in an opposite direction to the inner part (petals), and while the outer part accelerates, the inner part decelerates. We outline the concepts theoretically and confirm them experimentally. Such exotic structured light beams are topical due to their many applications, for instance in optical trapping and tweezing, metrology, and fundamental studies in optics.

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In 2000, monocular vision was restored to M. M., who had been blind between the ages of 3 and 46 years. Tests carried out over 2 years following the surgery revealed impairments of 3-D form, object, and face processing and an absence of object- and face-selective blood-oxygen-level-dependent responses in ventral visual cortex. In the present research, we reexamined M. M. to test for experience-dependent recovery of visual function. Behaviorally, M. M. remains impaired in 3-D form, object, and face processing. Accordingly, we found little to no evidence of the category-selective organization within ventral visual cortex typically associated with face, body, scene, or object processing. We did observe remarkably normal object selectivity within lateral occipital cortex, consistent with M. M.'s previously reported shape-discrimination performance. Together, these findings provide little evidence for recovery of high-level visual function after more than a decade of visual experience in adulthood.

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In a widely used functional magnetic resonance imaging (fMRI) data analysis method, functional regions of interest (fROIs) are handpicked in each participant using macroanatomic landmarks as guides, and the response of these regions to new conditions is then measured. A key limitation of this standard handpicked fROI method is the subjectivity of decisions about which clusters of activated voxels should be treated as the particular fROI in question in each subject. Here we apply the Group-Constrained Subject-Specific (GSS) method for defining fROIs, recently developed for identifying language fROIs (Fedorenko et al., 2010), to algorithmically identify fourteen well-studied category-selective regions of the ventral visual pathway (Kanwisher, 2010). We show that this method retains the benefit of defining fROIs in individual subjects without the subjectivity inherent in the traditional handpicked fROI approach. The tools necessary for using this method are available on our website (http://web.mit.edu/bcs/nklab/GSS.shtml).

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