Characteristic BOLD signals are detectable in white matter of the spinal cord at rest and after a stimulus.

We report the reliable detection of reproducible patterns of blood-oxygenation-level-dependent (BOLD) MRI signals within the white matter (WM) of the spinal cord during a task and in a resting state. Previous functional MRI studies have shown that BOLD signals are robustly detectable not only in gray matter (GM) in the brain but also in cerebral WM as well as the GM within the spinal cord, but similar signals in WM of the spinal cord have been overlooked. In this study, we detected BOLD signals in the WM of the spinal cord in squirrel monkeys and studied their relationships with the locations and functions of ascending and descending WM tracts. Tactile sensory stimulus -evoked BOLD signal changes were detected in the ascending tracts of the spinal cord using a general-linear model. Power spectral analysis confirmed that the amplitude at the fundamental frequency of the response to a periodic stimulus was significantly higher in the ascending tracts than the descending ones. Independent component analysis of resting-state signals identified coherent fluctuations from eight WM hubs which correspond closely to the known anatomical locations of the major WM tracts. Resting-state analyses showed that the WM hubs exhibited correlated signal fluctuations across spinal cord segments in reproducible patterns that correspond well with the known neurobiological functions of WM tracts in the spinal cord. Overall, these findings provide evidence of a functional organization of intraspinal WM tracts and confirm that they produce hemodynamic responses similar to GM both at baseline and under stimulus conditions.

Increased regional Hurst exponent reflects response inhibition related neural complexity alterations in pediatric bipolar disorder patients during an emotional Go-Nogo task.

Fractal patterns have been shown to change in resting- and task-state blood oxygen level-dependent signals in bipolar disorder patients. However, fractal characteristics of brain blood oxygen level-dependent signals when responding to external emotional stimuli in pediatric bipolar disorder remain unclear. Blood oxygen level-dependent signals of 20 PBD-I patients and 17 age- and sex-matched healthy controls were extracted while performing an emotional Go-Nogo task. Neural responses relevant to the task and Hurst exponent of the blood oxygen level-dependent signals were assessed. Correlations between clinical indices and Hurst exponent were estimated. Significantly increased activations were found in regions covering the frontal lobe, parietal lobe, temporal lobe, insula, and subcortical nuclei in PBD-I patients compared to healthy controls in contrast of emotional versus neutral distractors. PBD-I patients exhibited higher Hurst exponent in regions that involved in action control, such as superior frontal gyrus, inferior frontal gyrus, inferior temporal gyrus, and insula, with Hurst exponent of frontal orbital gyrus correlated with onset age. The present study exhibited overactivation, increased self-similarity and decreased complexity in cortical regions during emotional Go-Nogo task in patients relative to healthy controls, which provides evidence of an altered emotional modulation of cognitive control in pediatric bipolar disorder patients. Hurst exponent may be a fractal biomarker of neural activity in pediatric bipolar disorder.

Simultaneous cortical, subcortical, and brainstem mapping of sensory activation.

Nonpainful tactile sensory stimuli are processed in the cortex, subcortex, and brainstem. Recent functional magnetic resonance imaging studies have highlighted the value of whole-brain, systems-level investigation for examining sensory processing. However, whole-brain functional magnetic resonance imaging studies are uncommon, in part due to challenges with signal to noise when studying the brainstem. Furthermore, differentiation of small sensory brainstem structures such as the cuneate and gracile nuclei necessitates high-resolution imaging. To address this gap in systems-level sensory investigation, we employed a whole-brain, multi-echo functional magnetic resonance imaging acquisition at 3T with multi-echo independent component analysis denoising and brainstem-specific modeling to enable detection of activation across the entire sensory system. In healthy participants, we examined patterns of activity in response to nonpainful brushing of the right hand, left hand, and right foot (n = 10 per location), and found the expected lateralization, with distinct cortical and subcortical responses for upper and lower limb stimulation. At the brainstem level, we differentiated the adjacent cuneate and gracile nuclei, corresponding to hand and foot stimulation respectively. Our findings demonstrate that simultaneous cortical, subcortical, and brainstem mapping at 3T could be a key tool to understand the sensory system in both healthy individuals and clinical cohorts with sensory deficits.

Toward precision brain health: accurate prediction of a cognitive index trajectory using neuroimaging metrics.

The goal of precision brain health is to accurately predict individuals' longitudinal patterns of brain change. We trained a machine learning model to predict changes in a cognitive index of brain health from neurophysiologic metrics. A total of 48 participants (ages 21-65) completed a sensorimotor task during 2 functional magnetic resonance imaging sessions 6 mo apart. Hemodynamic response functions (HRFs) were parameterized using traditional (amplitude, dispersion, latency) and novel (curvature, canonicality) metrics, serving as inputs to a neural network model that predicted gain on indices of brain health (cognitive factor scores) for each participant. The optimal neural network model successfully predicted substantial gain on the cognitive index of brain health with 90% accuracy (determined by 5-fold cross-validation) from 3 HRF parameters: amplitude change, dispersion change, and similarity to a canonical HRF shape at baseline. For individuals with canonical baseline HRFs, substantial gain in the index is overwhelmingly predicted by decreases in HRF amplitude. For individuals with non-canonical baseline HRFs, substantial gain in the index is predicted by congruent changes in both HRF amplitude and dispersion. Our results illustrate that neuroimaging measures can track cognitive indices in healthy states, and that machine learning approaches using novel metrics take important steps toward precision brain health.

fMRI spectral signatures of sleep.

Sleep can be distinguished from wake by changes in brain electrical activity, typically assessed using electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by spindles and slow waves. Here we identified signatures of sleep in brain hemodynamic activity, using simultaneous functional MRI (fMRI) and EEG. We found that, at the transition from wake to sleep, fMRI blood oxygen level-dependent (BOLD) activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency (<0.1 Hz) oscillation prominent in light sleep and correlated with the occurrence of spindles, and a high-frequency oscillation (>0.1 Hz) prominent in deep sleep and correlated with the occurrence of slow waves. The two oscillations were both detectable across the brain but exhibited distinct spatiotemporal patterns. During the falling-asleep process, the low-frequency oscillation first appeared in the thalamus, then the posterior cortex, and lastly the frontal cortex, while the high-frequency oscillation first appeared in the midbrain, then the frontal cortex, and lastly the posterior cortex. During the waking-up process, both oscillations disappeared first from the thalamus, then the frontal cortex, and lastly the posterior cortex. The BOLD oscillations provide local signatures of spindle and slow wave activity. They may be employed to monitor the regional occurrence of sleep or wakefulness, track which regions are the first to fall asleep or wake up at the wake-sleep transitions, and investigate local homeostatic sleep processes.

Texture-like representation of objects in human visual cortex.

The human visual ability to recognize objects and scenes is widely thought to rely on representations in category-selective regions of the visual cortex. These representations could support object vision by specifically representing objects, or, more simply, by representing complex visual features regardless of the particular spatial arrangement needed to constitute real-world objects, that is, by representing visual textures. To discriminate between these hypotheses, we leveraged an image synthesis approach that, unlike previous methods, provides independent control over the complexity and spatial arrangement of visual features. We found that human observers could easily detect a natural object among synthetic images with similar complex features that were spatially scrambled. However, observer models built from BOLD responses from category-selective regions, as well as a model of macaque inferotemporal cortex and Imagenet-trained deep convolutional neural networks, were all unable to identify the real object. This inability was not due to a lack of signal to noise, as all observer models could predict human performance in image categorization tasks. How then might these texture-like representations in category-selective regions support object perception? An image-specific readout from category-selective cortex yielded a representation that was more selective for natural feature arrangement, showing that the information necessary for natural object discrimination is available. Thus, our results suggest that the role of the human category-selective visual cortex is not to explicitly encode objects but rather to provide a basis set of texture-like features that can be infinitely reconfigured to flexibly learn and identify new object categories.

Development of BOLD Response to Motion in Human Infants.

Behavioral studies suggest that motion perception is rudimentary at birth and matures steadily over the first few years. We demonstrated previously that the major cortical associative areas serving motion processing, like middle temporal complex (MT+), visual cortex area 6 (V6), and PIVC in adults, show selective responses to coherent flow in 8-week-old infants. Here, we study the BOLD response to the same motion stimuli in 5-week-old infants (four females and four males) and compare the maturation between these two ages. The results show that MT+ and PIVC areas show a similar motion response at 5 and 8 weeks, whereas response in the V6 shows a reduced BOLD response to motion at 5 weeks, and cuneus associative areas are not identifiable at this young age. In infants and in adults, primary visual cortex (V1) does not show a selectivity for coherent motion but shows very fast development between 5 and 8 weeks of age in response to the appearance of motion stimuli. Resting-state correlations demonstrate adult-like functional connectivity between the motion-selective associative areas but not between primary cortex and temporo-occipital and posterior-insular cortices. The results are consistent with a differential developmental trajectory of motion area respect to other occipital regions, probably reflecting also a different development trajectory of the central and peripheral visual field.SIGNIFICANCE STATEMENT How the cortical visual areas attain the specialization that we observed in human adults in the first few months of life is unknown. However, this knowledge is crucial to understanding the consequence of perinatal brain damage and its outcome. Here, we show that motion selective areas are already functioning well in 5-week-old infants with greater responses for detecting coherent motion over random motion, suggesting that very little experience is needed to attain motion selectivity.

On the interplay between state-dependent reconfigurations of global signal correlation and BOLD fluctuations: An fMRI study.

BACKGROUND: The dynamics of global, state-dependent reconfigurations in brain connectivity are yet unclear. We aimed at assessing reconfigurations of the global signal correlation coefficient (GSCORR), a measure of the connectivity between each voxel timeseries and the global signal, from resting-state to a stop-signal task. The secondary aim was to assess the relationship between GSCORR and blood-oxygen-level-dependent (BOLD) activations or deactivation across three different trial-conditions (GO, STOP-correct, and STOP-incorrect). METHODS: As primary analysis we computed whole-brain, voxel-wise GSCORR during resting-state (GSCORR-rest) and stop-signal task (GSCORR-task) in 107 healthy subjects aged 21-50, deriving GSCORR-shift as GSCORR-task minus GSCORR-rest. GSCORR-tr and trGSCORR-shift were also computed on the task residual time series to quantify the impact of the task-related activity during the trials. To test the secondary aim, brain regions were firstly divided in one cluster showing significant task-related activation and one showing significant deactivation across the three trial conditions. Then, correlations between GSCORR-rest/task/shift and activation/deactivation in the two clusters were computed. As sensitivity analysis, GSCORR-shift was computed on the same sample after performing a global signal regression and GSCORR-rest/task/shift were correlated with the task performance. RESULTS: Sensory and temporo-parietal regions exhibited a negative GSCORR-shift. Conversely, associative regions (ie. left lingual gyrus, bilateral dorsal posterior cingulate gyrus, cerebellum areas, thalamus, posterolateral parietal cortex) displayed a positive GSCORR-shift (FDR-corrected p < 0.05). GSCORR-shift showed similar patterns to trGSCORR-shift (magnitude increased) and after global signal regression (magnitude decreased). Concerning BOLD changes, Brodmann area 6 and inferior parietal lobule showed activation, while posterior parietal lobule, cuneus, precuneus, middle frontal gyrus showed deactivation (FDR-corrected p < 0.05). No correlations were found between GSCORR-rest/task/shift and beta-coefficients in the activation cluster, although negative correlations were observed between GSCORR-task and GO/STOP-correct deactivation (Pearson rho=-0.299/-0.273; Bonferroni-p < 0.05). Weak associations between GSCORR and task performance were observed (uncorrected p < 0.05). CONCLUSION: GSCORR state-dependent reconfiguration indicates a reallocation of functional resources to associative areas during stop-signal task. GSCORR, activation and deactivation may represent distinct proxies of brain states with specific neurofunctional relevance.

Breath-hold BOLD fMRI without CO2 sampling enables estimation of venous cerebral blood volume: potential use in normalization of stimulus-evoked BOLD fMRI data.

BOLD fMRI signal has been used in conjunction with vasodilatory stimulation as a marker of cerebrovascular reactivity (CVR): the relative change in cerebral blood flow (CBF) arising from a unit change in the vasodilatory stimulus. Using numerical simulations, we demonstrate that the variability in the relative BOLD signal change induced by vasodilation is strongly influenced by the variability in deoxyhemoglobin-containing cerebral blood volume (CBV), as this source of variability is likely to be more prominent than that of CVR. It may, therefore, be more appropriate to describe the relative BOLD signal change induced by an isometabolic vasodilation as a proxy of deoxygenated CBV (CBVdHb) rather than CVR. With this in mind, a new method was implemented to map a marker of CBVdHb, termed BOLD-CBV, based on the normalization of voxel-wise BOLD signal variation by an estimate of the intravascular venous BOLD signal from voxels filled with venous blood. The intravascular venous BOLD signal variation, recorded during repeated breath-holding, was extracted from the superior sagittal sinus in a cohort of 27 healthy volunteers and used as a regressor across the whole brain, yielding maps of BOLD-CBV. In the same cohort, we demonstrated the potential use of BOLD-CBV for the normalization of stimulus-evoked BOLD fMRI by comparing group-level BOLD fMRI responses to a visuomotor learning task with and without the inclusion of voxel-wise vascular covariates of BOLD-CBV and the BOLD signal change per mmHg variation in end-tidal carbon dioxide (BOLD-CVR). The empirical measure of BOLD-CBV accounted for more between-subject variability in the motor task-induced BOLD responses than BOLD-CVR estimated from end-tidal carbon dioxide recordings. The new method can potentially increase the power of group fMRI studies by including a measure of vascular characteristics and has the strong practical advantage of not requiring experimental measurement of end-tidal carbon dioxide, unlike traditional methods to estimate BOLD-CVR. It also more closely represents a specific physiological characteristic of brain vasculature than BOLD-CVR, namely blood volume.

Dynamic Functional Connectivity in Pediatric Mild Traumatic Brain Injury.

Resting-state fMRI can be used to identify recurrent oscillatory patterns of functional connectivity within the human brain, also known as dynamic brain states. Alterations in dynamic brain states are highly likely to occur following pediatric mild traumatic brain injury (pmTBI) due to the active developmental changes. The current study used resting-state fMRI to investigate dynamic brain states in 200 patients with pmTBI (ages 8-18 years, median = 14 years) at the subacute (∼1-week post-injury) and early chronic (∼ 4 months post-injury) stages, and in 179 age- and sex-matched healthy controls (HC). A k-means clustering analysis was applied to the dominant time-varying phase coherence patterns to obtain dynamic brain states. In addition, correlations between brain signals were computed as measures of static functional connectivity. Dynamic connectivity analyses showed that patients with pmTBI spend less time in a frontotemporal default mode/limbic brain state, with no evidence of change as a function of recovery post-injury. Consistent with models showing traumatic strain convergence in deep grey matter and midline regions, static interhemispheric connectivity was affected between the left and right precuneus and thalamus, and between the right supplementary motor area and contralateral cerebellum. Changes in static or dynamic connectivity were not related to symptom burden or injury severity measures, such as loss of consciousness and post-traumatic amnesia. In aggregate, our study shows that brain dynamics are altered up to 4 months after pmTBI, in brain areas that are known to be vulnerable to TBI. Future longitudinal studies are warranted to examine the significance of our findings in terms of long-term neurodevelopment.

fMRI, LFP, and anatomical evidence for hierarchical nociceptive routing pathway between somatosensory and insular cortices.

The directional organization of multiple nociceptive regions, particularly within obscure operculoinsular areas, underlying multidimensional pain processing remains elusive. This study aims to establish the fundamental organization between somatosensory and insular cortices in routing nociceptive information. By employing an integrated multimodal approach of high-field fMRI, intracranial electrophysiology, and transsynaptic viral tracing in rats, we observed a hierarchically organized connection of S1/S2 → posterior insula → anterior insula in routing nociceptive information. The directional nociceptive pathway determined by early fMRI responses was consistent with that examined by early evoked LFP, intrinsic effective connectivity, and anatomical projection, suggesting fMRI could provide a valuable facility to discern directional neural circuits in animals and humans non-invasively. Moreover, our knowledge of the nociceptive hierarchical organization of somatosensory and insular cortices and the interface role of the posterior insula may have implications for the development of targeted pain therapies.

Association between neurovascular coupling and neural signals in the resting state as revealed by a combined fMRI and magnetoencephalography study in humans.

The blood oxygenation level-dependent (BOLD) activation reflects hemodynamic events mediated by neurovascular coupling. During task performance, the BOLD hemodynamic response in a relevant area is mainly driven by the high levels of synaptic activity (reflected in local field potentials, LFPs) but, in contrast, during a task-free, resting state, the contribution to BOLD of such neural events is small, as expected by the comparatively (to the task state) low level of neural events. Concomitant recording of BOLD and LFP at rest in animal experiments has estimated the neural contribution to BOLD to ∼10%. Such experiments have not been performed in humans. As an approximation, we recorded (in the same subject, n = 57 healthy participants) at a task-free, resting state the BOLD signal and, in a different session, the magnetoencephalographic (MEG) signal, which reflects purely neural (synaptic) events. We then calculated the turnover of these signals by computing the successive moment-to-moment difference in the BOLD and MEG time series and retaining the median of the absolute value of the differenced series (BOLD and TMEG, respectively). The correlation between normalized turnovers of BOLD (TBOLD) and turnovers of MEG (TMEG) was r = 0.336 (r2 = 0.113; P = 0.011). These results estimate that 11.3% of the variance in TBOLD can be explained by the variance in TMEG. This estimate is close to the aforementioned estimate obtained by direct recordings in animal experiments. NEW & NOTEWORTHY Here, we report on a weak positive association between turnovers of blood oxygenation level-dependent (TBOLD) and magnetoencephalographic (TMEG) signals in 57 healthy human subjects in a resting, task-free state. More specifically, we found that the purely neural TMEG accounted for 11.1% of the TBOLD, a percentage remarkably close to that found between resting-state local field potentials (LFPs) and BOLD recorded concurrently in animal experiments.

Negative association between neurovascular coupling and cortical gray matter volume during the lifespan.

Recent studies have established the moment-to-moment turnover of the blood-oxygen-level-dependent signal (TBOLD) at resting state as a key measure of local cortical brain function. Here, we sought to extend that line of research by evaluating TBOLD in 70 cortical areas with respect to corresponding brain volume, age, and sex across the lifespan in 1,344 healthy participants including 633 from the Human Connectome Project (HCP)-Development cohort (294 males and 339 females, age range 8-21 yr) and 711 healthy participants from HCP-Aging cohort (316 males and 395 females, 36-90 yr old). In both groups, we found that 1) TBOLD increased with age, 2) volume decreased with age, and 3) TBOLD and volume were highly significantly negatively correlated, independent of age. The inverse association between TBOLD and volume was documented in nearly all 70 brain areas and for both sexes, with slightly stronger associations documented for males. The strong correspondence between TBOLD and volume across age and sex suggests a common influence such as chronic neuroinflammation contributing to reduced cortical volume and increased TBOLD across the lifespan.NEW & NOTEWORTHY We report a significant negative association between resting functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signal turnover (TBOLD) and cortical gray matter volume across the lifespan, such that TBOLD increased whereas volume decreased. We attribute this association to a hypothesized chronic, low-grade neuroinflammation, probably induced by various neurotropic pathogens, including human herpes viruses known to be dormant in the brain in a latent state and reactivated by stress, fever, and various environmental exposures, such as ultraviolet light.

How do tasks impact the reliability of fMRI functional connectivity?

While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.

Reliability of variability and complexity measures for task and task-free BOLD fMRI.

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

Precision fMRI and cluster-failure in the individual brain.

Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal-to-noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level-dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single-subject analysis. We introduce adaptive-weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster-corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole-brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family-wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download.

Parafoveal vision reveals qualitative differences between fusiform face area and parahippocampal place area.

The center-periphery visual field axis guides early visual system organization with enhanced resources devoted to central vision leading to reduced peripheral performance relative to that of central vision (i.e., behavioral eccentricity effect) for many visual functions. The center-periphery organization extends to high-order visual cortex where, for example, the well-studied face-sensitive fusiform face area (FFA) shows sensitivity to central vision and the place-sensitive parahippocampal place area (PPA) shows sensitivity to peripheral vision. As we have recently found that face perception is more sensitive to eccentricity than place perception, here we examined whether these behavioral findings reflect differences in FFA's and PPA's sensitivities to eccentricity. We assumed FFA would show higher sensitivity to eccentricity than PPA would, but that both regions' modulation by eccentricity would be invariant to the viewed category. We parametrically investigated (fMRI, n = 32) how FFA's and PPA's activations are modulated by eccentricity (≤8°) and category (upright/inverted faces/houses) while keeping stimulus size constant. As expected, FFA showed an overall higher sensitivity to eccentricity than PPA. However, both regions' activation modulations by eccentricity were dependent on the viewed category. In FFA, a reduction of activation with growing eccentricity ("BOLD eccentricity effect") was found (with different amplitudes) for all categories. In PPA however, qualitatively different BOLD eccentricity effect modulations were found (e.g., at 8° mild BOLD eccentricity effect for houses but a reverse BOLD eccentricity effect for faces and no modulation for inverted faces). Our results emphasize that peripheral vision investigations are critical to further our understanding of visual processing.

Sub-bundle based analysis reveals the role of human optic radiation in visual working memory.

White matter (WM) functional activity has been reliably detected through functional magnetic resonance imaging (fMRI). Previous studies have primarily examined WM bundles as unified entities, thereby obscuring the functional heterogeneity inherent within these bundles. Here, for the first time, we investigate the function of sub-bundles of a prototypical visual WM tract-the optic radiation (OR). We use the 7T retinotopy dataset from the Human Connectome Project (HCP) to reconstruct OR and further subdivide the OR into sub-bundles based on the fiber's termination in the primary visual cortex (V1). The population receptive field (pRF) model is then applied to evaluate the retinotopic properties of these sub-bundles, and the consistency of the pRF properties of sub-bundles with those of V1 subfields is evaluated. Furthermore, we utilize the HCP working memory dataset to evaluate the activations of the foveal and peripheral OR sub-bundles, along with LGN and V1 subfields, during 0-back and 2-back tasks. We then evaluate differences in 2bk-0bk contrast between foveal and peripheral sub-bundles (or subfields), and further examine potential relationships between 2bk-0bk contrast and 2-back task d-prime. The results show that the pRF properties of OR sub-bundles exhibit standard retinotopic properties and are typically similar to the properties of V1 subfields. Notably, activations during the 2-back task consistently surpass those under the 0-back task across foveal and peripheral OR sub-bundles, as well as LGN and V1 subfields. The foveal V1 displays significantly higher 2bk-0bk contrast than peripheral V1. The 2-back task d-prime shows strong correlations with 2bk-0bk contrast for foveal and peripheral OR fibers. These findings demonstrate that the blood oxygen level-dependent (BOLD) signals of OR sub-bundles encode high-fidelity visual information, underscoring the feasibility of assessing WM functional activity at the sub-bundle level. Additionally, the study highlights the role of OR in the top-down processes of visual working memory beyond the bottom-up processes for visual information transmission. Conclusively, this study innovatively proposes a novel paradigm for analyzing WM fiber tracts at the individual sub-bundle level and expands understanding of OR function.

Estimation of static and dynamic functional connectivity in resting-state fMRI using zero-frequency resonator.

Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level-dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs-fMRI is spontaneous event-related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low-valued inflection points. Here, we extract the event-related temporal onsets from the rs-fMRI time courses using a zero-frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal-to-noise ratio (SNR) segments are also obtained in the rs-fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR-based CR distinguishes the coactivation and non-coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR-driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.

Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end-to-end frequency-specific dual-attention-based adversarial network (FDAA-Net) to extend the time series of existing blood oxygen level-dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency-dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial-temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA-Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA-Net effectively overcame linear frequency-specific challenges and outperformed other popular prediction models. Test-retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA-Net using short-term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.