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Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The task-evoked positive BOLD response (PBR) to a unilateral visual hemi-field stimulation is often accompanied by robust and sustained contralateral as well as ipsilateral negative BOLD responses (NBRs) in the visual cortex. The signal characteristics and the neural and/or vascular mechanisms that underlie these two types of NBRs are not completely understood. In this paper, we investigated the properties of these two types of NBRs. We first demonstrated the linearity of both NBRs with respect to stimulus duration. Next, we showed that the hemodynamic response functions (HRFs) of the two NBRs were similar to each other, but significantly different from that of the PBR. Moreover, the subject-wise expressions of the two NBRs were tightly coupled to the degree that the correlation between the two NBRs was significantly higher than the correlation between each NBR and the PBR. However, the activation patterns of the two NBRs did not show a high level of interhemispheric spatial similarity, and the functional connectivity between them was not different than the interhemispheric functional connectivity between the NBRs and PBR. Finally, while attention did modulate both NBRs, the attention-related changes in their HRFs were similar. Our findings suggest that the two NBRs might be generated through common neural and/or vascular mechanisms involving distal/deep brain regions that project to the two hemispheres., Competing Interests: Declaration of Competing Interest None, (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Previous attempts at characterizing the spatial specificity of the blood oxygenation level dependent functional MRI (BOLD fMRI) response by estimating its point-spread function (PSF) have conventionally relied on retinotopic spatial representations of visual stimuli in area V1. Consequently, their estimates were confounded by the width and scatter of receptive fields of V1 neurons. Here, we circumvent these limits by instead using the inherent cortical spatial organization of ocular dominance columns (ODCs) to determine the PSF for both Gradient Echo (GE) and Spin Echo (SE) BOLD imaging at 7 Tesla. By applying Markov chain Monte Carlo sampling on a probabilistic generative model of imaging ODCs, we quantified the PSFs that best predict the spatial structure and magnitude of differential ODCs' responses. Prior distributions for the ODC model parameters were determined by analyzing published data of cytochrome oxidase patterns from post-mortem histology of human V1 and of neurophysiological ocular dominance indices. The average PSF full-widths at half-maximum obtained from differential ODCs' responses following the removal of voxels influenced by contributions from macroscopic blood vessels were 0.86 mm (SE) and 0.99 mm (GE). Our results provide a quantitative basis for the spatial specificity of BOLD fMRI at ultra-high fields, which can be used for planning and interpretation of high-resolution differential fMRI of fine-scale cortical organizations., (Copyright © 2017. Published by Elsevier Inc.)

The capacity of functional MRI (fMRI) to resolve cortical columns depends on several factors. These include the spatial scale of the columnar pattern, the point-spread of the fMRI response, the voxel size, and the signal-to-noise ratio (SNR) considering thermal and physiological noise. However, it remains unknown how these factors combine, and what is the voxel size that optimizes fMRI of cortical columns. Here we combine current knowledge into a quantitative model of fMRI of realistic patterns of cortical columns with different spatial scales and degrees of irregularity. We compare different approaches for identifying patterns of cortical columns, including univariate and multivariate based detection, multi-voxel pattern analysis (MVPA) based decoding, and high-resolution imaging and reconstruction of the pattern of cortical columns. We present the dependence of the performance of each approach on the parameters of the imaged pattern as well as those of the data acquisition. In addition, we predict voxel sizes that optimize fMRI of cortical columns under various scenarios. We found that all measures associated with multivariate detection and decoding could be approximately calculated from a measure we termed "multivariate contrast-to-noise ratio" (mv-CNR), which is a function of the contrast-to-noise ratio (CNR) and number of voxels. Furthermore, mv-CNR implied that the optimal voxel width for detection and decoding is independent of changes in response amplitude, SNR and imaged volume that are not caused by changes in voxel size. For regular patterns, optimal voxel widths for detection, decoding and imaging/reconstructing the pattern of cortical columns were approximately half the main cycle length of the organization. Optimal voxel widths for irregular patterns were less dependent on the main cycle length, and differed between univariate detection, multivariate detection and decoding, and reconstruction. We compared the effects of different factors of Gradient Echo fMRI at 3 Tesla (T), Gradient Echo fMRI at 7T, and Spin-Echo fMRI at 7T on the detection, decoding, and reconstruction measures considered and found that in all cases the width of the fMRI point-spread had the most significant effect. In contrast, different response amplitudes and noise characteristics played a relatively minor role. We recommend specific voxel widths for optimal univariate detection, for multivariate detection and decoding, and for high-resolution imaging of cortical columns under these three data-acquisition scenarios. Our study supports the planning, optimization, and interpretation of high-resolution fMRI of cortical columns and the decoding of information conveyed by these columns., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Numerous studies have demonstrated functional magnetic resonance imaging (fMRI)-based resting-state functional connectivity (RSFC) between cortical areas. Recent evidence suggests that synchronous fluctuations in blood oxygenation level-dependent fMRI reflect functional organization at a scale finer than that of visual areas. In this study, we investigated whether RSFCs within and between lower visual areas are retinotopically organized and whether retinotopically organized RSFC merely reflects cortical distance. Subjects underwent retinotopic mapping and separately resting-state fMRI. Visual areas V1, V2, and V3, were subdivided into regions of interest (ROIs) according to quadrants and visual field eccentricity. Functional connectivity (FC) was computed based on Pearson's linear correlation (correlation), and Pearson's linear partial correlation (correlation between two time courses after the time courses from all other regions in the network are regressed out). Within a quadrant, within visual areas, all correlation and nearly all partial correlation FC measures showed statistical significance. Consistently in V1, V2, and to a lesser extent in V3, correlation decreased with increasing eccentricity separation. Consistent with previously reported monkey anatomical connectivity, correlation/partial correlation values between regions from adjacent areas (V1-V2 and V2-V3) were higher than those between nonadjacent areas (V1-V3). Within a quadrant, partial correlation showed consistent significance between regions from two different areas with the same or adjacent eccentricities. Pairs of ROIs with similar eccentricity showed higher correlation/partial correlation than pairs distant in eccentricity. Between dorsal and ventral quadrants, partial correlation between common and adjacent eccentricity regions within a visual area showed statistical significance; this extended to more distant eccentricity regions in V1. Within and between quadrants, correlation decreased approximately linearly with increasing distances separating the tested ROIs. Partial correlation showed a more complex dependence on cortical distance: it decreased exponentially with increasing distance within a quadrant, but was best fit by a quadratic function between quadrants. We conclude that RSFCs within and between lower visual areas are retinotopically organized. Correlation-based FC is nonselectively high across lower visual areas, even between regions that do not share direct anatomical connections. The mechanisms likely involve network effects caused by the dense anatomical connectivity within this network and projections from higher visual areas. FC based on partial correlation, which minimizes network effects, follows expectations based on direct anatomical connections in the monkey visual cortex better than correlation. Last, partial correlation-based retinotopically organized RSFC reflects more than cortical distance effects.

Executive dysfunction is frequently associated with episodic memory decline in amnestic mild cognitive impairment (aMCI) patients. Resting state executive control network (RS-ECN) represents a novel approach to interrogate the integrity of brain areas underlying executive dysfunction. The present study aims to investigate RS-ECN in aMCI and examine a possible link between changes in brain functional connectivity and declines in executive function. aMCI individuals (n = 13) and healthy subjects (n = 16) underwent cognitive assessment including executive function and high field functional magnetic resonance imaging. Individual RS-ECN maps were estimated using a seed-based cross-correlation method. Between groups RS-ECN functional connectivity comparison was assessed using voxel-wise statistic parametric mapping. aMCI individuals had reduced RS-ECN connectivity in the anterior cingulate cortex (ACC) and dorsal lateral prefrontal cortex (DLPFC), bilaterally. In contrast, aMCI showed increased connectivity in ventral lateral and anterior prefrontal cortex, bilaterally. Connectivity strength was associated with executive function in the ACC (r = 0.6213, p = 0.023) and right DLPFC (r = 0.6454, p = 0.017). Coexistence between connectivity declines and recruitment of brain regions outside the RS-ECN as reported here fits a brain reserve conceptual framework in which brain networks undergo remodeling in aMCI individuals.

Multivariate machine learning algorithms applied to human functional MRI (fMRI) data can decode information conveyed by cortical columns, despite the voxel-size being large relative to the width of columns. Several mechanisms have been proposed to underlie decoding of stimulus orientation or the stimulated eye. These include: (I) aliasing of high spatial-frequency components, including the main frequency component of the columnar organization, (II) contributions from local irregularities in the columnar organization, (III) contributions from large-scale non-columnar organizations, (IV) functionally selective veins with biased draining regions, and (V) complex spatio-temporal filtering of neuronal activity by fMRI voxels. Here we sought to assess the plausibility of two of the suggested mechanisms: (I) aliasing and (II) local irregularities, using a naive model of BOLD as blurring and MRI voxel sampling. To this end, we formulated a mathematical model that encompasses both the processes of imaging ocular dominance (OD) columns and the subsequent linear classification analysis. Through numerical simulations of the model, we evaluated the distribution of functional differential contrasts that can be expected when considering the pattern of cortical columns, the hemodynamic point spread function, the voxel size, and the noise. We found that with data acquisition parameters used at 3 Tesla, sub-voxel supra-Nyquist frequencies, including frequencies near the main frequency of the OD organization (0.5 cycles per mm), cannot contribute to the differential contrast. The differential functional contrast of local origin is dominated by low-amplitude contributions from low frequencies, associated with irregularities of the cortical pattern. Realizations of the model with parameters that reflected best-case scenario and the reported BOLD point-spread at 3 Tesla (3.5mm) predicted decoding performances lower than those that have been previously obtained at this magnetic field strength. We conclude that low frequency components that underlie local irregularities in the columnar organization are likely to play a role in decoding. We further expect that fMRI-based decoding relies, in part, on signal contributions from large-scale, non-columnar functional organizations, and from complex spatio-temporal filtering of neuronal activity by fMRI voxels, involving biased venous responses. Our model can potentially be used for evaluating and optimizing data-acquisition parameters for decoding information conveyed by cortical columns., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Recent studies have demonstrated that multivariate machine learning algorithms applied to human functional MRI data can decode information segregated in cortical columns, despite the voxel size being large relative to the width of columns. The mechanism by which low spatial resolution imaging decodes information represented in a fine-scale organization is not clear. To investigate mechanisms underlying decoding signals we employed high-resolution gradient-echo BOLD functional MRI of visual area V1. We show that in addition to the fine-scale ocular dominance columns, coarse-scale structures extending over several millimeters also convey discriminative power for decoding the stimulated eye. Discriminative power is conveyed by both macroscopic blood vessels and gray matter regions. We hypothesize that gray-matter regions which drain into specific vessels may preferentially contain ocular-dominance columns biased towards one eye; the bias of a specific region thereby causing a functionally selective ocular-dominance response in the associated vessel. Our findings indicate that coarse-scale structures and macroscopic blood vessels contribute to decoding of the stimulated eye based on low-resolution multivariate data., (Copyright (c) 2009 Elsevier Inc. All rights reserved.)

Recent studies have demonstrated large amplitude spontaneous fluctuations in functional-MRI (fMRI) signals in humans in the resting state. Importantly, these spontaneous fluctuations in blood-oxygenation-level-dependent (BOLD) signal are often synchronized over distant parts of the brain, a phenomenon termed functional-connectivity. Functional-connectivity is widely assumed to reflect interregional coherence of fluctuations in activity of the underlying neuronal networks. Despite the large body of human imaging literature on spontaneous activity and functional-connectivity in the resting state, the link to underlying neural activity remains tenuous. Through simultaneous fMRI and intracortical neurophysiological recording, we demonstrate correlation between slow fluctuations in BOLD signals and concurrent fluctuations in the underlying locally measured neuronal activity. This correlation varied with time-lag of BOLD relative to neuronal activity, resembling a traditional hemodynamic response function with peaks at approximately 6 s lag of BOLD signal. The correlations were reliably detected when the neuronal signal consisted of either the spiking rate of a small group of neurons, or relative power changes in the multi-unit activity band, and particularly in the local field potential gamma band. Analysis of correlation between the voxel-by-voxel fMRI time-series and the neuronal activity measured within one cortical site showed patterns of correlation that slowly traversed cortex. BOLD fluctuations in widespread areas in visual cortex of both hemispheres were significantly correlated with neuronal activity from a single recording site in V1. To the extent that our V1 findings can be generalized to other cortical areas, fMRI-based functional-connectivity between remote regions in the resting state can be linked to synchronization of slow fluctuations in the underlying neuronal signals., ((c) 2008 Wiley-Liss, Inc.)

Cortical maps and feedback connections are ubiquitous features of the visual cerebral cortex. The role of the feedback connections, however, is unclear. This study was aimed at revealing possible organizational relationships between the feedback projections from area V2 and the functional maps of orientation and retinotopy in area V1. Optical imaging of intrinsic signals was combined with cytochrome oxidase histochemistry and connectional anatomy in owl monkeys. Tracer injections were administered at orientation-selective domains in regions of pale and thick cytochrome oxidase stripes adjacent to the border between these stripes. The feedback projections from V2 were found to be more diffuse than the intrinsic horizontal connections within V1, but they nevertheless demonstrated clustering. The clusters of feedback axons projected preferentially to interblob cytochrome oxidase regions. The distribution of preferred orientations of the recipient domains in V1 was broad but appeared biased toward values similar to the preferred orientation of the projecting cells in V2. The global spatial distribution of the feedback projections in V1 was anisotropic. The major axis of anisotropy was systematically parallel to a retinotopic axis in V1 corresponding to the preferred orientation of the cells of origin in V2. We conclude that the feedback connections from V2 to V1 might play a role in enhancing the response in V1 to collinear contour elements.

• Seven brain regions show both reduced cortical thickness and abnormal QSM levels in AD patients. • We observed significant correlations across AD patients between region-specific cortical thickness and network topology. • Decreased correlation between QSM value and structural network efficiency in AD patients at individual level across brain regions with reduced cortical thickness and aberrant QSM value. Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD. [ABSTRACT FROM AUTHOR]