Event Integration and Temporal Differentiation: How Hierarchical Knowledge Emerges in Hippocampal Subfields through Learning.

Everyday life is composed of events organized by changes in contexts, with each event containing an unfolding sequence of occurrences. A major challenge facing our memory systems is how to integrate sequential occurrences within events while also maintaining their details and avoiding over-integration across different contexts. We asked if and how distinct hippocampal subfields come to hierarchically and, in parallel, represent both event context and subevent occurrences with learning. Female and male human participants viewed sequential events defined as sequences of objects superimposed on shared color frames while undergoing high-resolution fMRI. Importantly, these events were repeated to induce learning. Event segmentation, as indexed by increased reaction times at event boundaries, was observed in all repetitions. Temporal memory decisions were quicker for items from the same event compared to across different events, indicating that events shaped memory. With learning, hippocampal CA3 multivoxel activation patterns clustered to reflect the event context, with more clustering correlated with behavioral facilitation during event transitions. In contrast, in the dentate gyrus (DG), temporally proximal items that belonged to the same event became associated with more differentiated neural patterns. A computational model explained these results by dynamic inhibition in the DG. Additional similarity measures support the notion that CA3 clustered representations reflect shared voxel populations, while DG's distinct item representations reflect different voxel populations. These findings suggest an interplay between temporal differentiation in the DG and attractor dynamics in CA3. They advance our understanding of how knowledge is structured through integration and separation across time and context.

Differential Contributions of Ventral Striatum Subregions to the Motivational and Hedonic Components of the Affective Processing of Reward.

The ventral striatum is implicated in the affective processing of reward, which can be divided into a motivational and a hedonic component. Here, we examined whether these two components rely on distinct neural substrates within the ventral striatum in humans (11 females and 13 males). We used a high-resolution fMRI protocol targeting the ventral striatum combined with a pavlovian-instrumental task and a hedonic reactivity task. Both tasks involved an olfactory reward, thereby allowing us to measure pavlovian-triggered motivation and sensory pleasure for the same reward within the same participants. Our findings show that different subregions of the ventral striatum are dissociable in their contributions to the motivational versus the hedonic component of the affective processing of reward. Parsing the neural mechanisms of the interplay between pavlovian incentive and hedonic processes may have important implications for understanding compulsive reward-seeking behaviors such as addiction, binge eating, or gambling.

Interdigitated Columnar Representation of Personal Space and Visual Space in Human Parietal Cortex.

Personal space (PS) is the space around the body that people prefer to maintain between themselves and unfamiliar others. Intrusion into personal space evokes discomfort and an urge to move away. Physiologic studies in nonhuman primates suggest that defensive responses to intruding stimuli involve the parietal cortex. We hypothesized that the spatial encoding of interpersonal distance is initially transformed from purely sensory to more egocentric mapping within human parietal cortex. This hypothesis was tested using 7 Tesla (7T) fMRI at high spatial resolution (1.1 mm isotropic), in seven subjects (four females, three males). In response to visual stimuli presented at a range of virtual distances, we found two categories of distance encoding in two corresponding radially-extending columns of activity within parietal cortex. One set of columns (P columns) responded selectively to moving and stationary face images presented at virtual distances that were nearer (but not farther) than each subject's behaviorally-defined personal space boundary. In most P columns, BOLD response amplitudes increased monotonically and nonlinearly with increasing virtual face proximity. In the remaining P columns, BOLD responses decreased with increasing proximity. A second set of parietal columns (D columns) responded selectively to disparity-based distance cues (near or far) in random dot stimuli, similar to disparity-selective columns described previously in occipital cortex. Critically, in parietal cortex, P columns were topographically interdigitated (nonoverlapping) with D columns. These results suggest that visual spatial information is transformed from visual to body-centered (or person-centered) dimensions in multiple local sites within human parietal cortex.SIGNIFICANCE STATEMENT Recent COVID-related social distancing practices highlight the need to better understand brain mechanisms which regulate "personal space" (PS), which is defined by the closest interpersonal distance that is comfortable for an individual. Using high spatial resolution brain imaging, we tested whether a map of external space is transformed from purely visual (3D-based) information to a more egocentric map (related to personal space) in human parietal cortex. We confirmed this transformation and further showed that it was mediated by two mutually segregated sets of columns: one which encoded interpersonal distance and another that encoded visual distance. These results suggest that the cortical transformation of sensory-centered to person-centered encoding of space near the body involves short-range communication across interdigitated columns within parietal cortex.

Cortical depth profiles of auditory and visual 7 T functional MRI responses in human superior temporal areas.

Invasive neurophysiological studies in nonhuman primates have shown different laminar activation profiles to auditory vs. visual stimuli in auditory cortices and adjacent polymodal areas. Means to examine the underlying feedforward vs. feedback type influences noninvasively have been limited in humans. Here, using 1-mm isotropic resolution 3D echo-planar imaging at 7 T, we studied the intracortical depth profiles of functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) signals to brief auditory (noise bursts) and visual (checkerboard) stimuli. BOLD percent-signal-changes were estimated at 11 equally spaced intracortical depths, within regions-of-interest encompassing auditory (Heschl's gyrus, Heschl's sulcus, planum temporale, and posterior superior temporal gyrus) and polymodal (middle and posterior superior temporal sulcus) areas. Effects of differing BOLD signal strengths for auditory and visual stimuli were controlled via normalization and statistical modeling. The BOLD depth profile shapes, modeled with quadratic regression, were significantly different for auditory vs. visual stimuli in auditory cortices, but not in polymodal areas. The different depth profiles could reflect sensory-specific feedforward versus cross-sensory feedback influences, previously shown in laminar recordings in nonhuman primates. The results suggest that intracortical BOLD profiles can help distinguish between feedforward and feedback type influences in the human brain. Further experimental studies are still needed to clarify how underlying signal strength influences BOLD depth profiles under different stimulus conditions.

Memory Reactivation during Learning Simultaneously Promotes Dentate Gyrus/CA2,3 Pattern Differentiation and CA1 Memory Integration.

Events that overlap with previous experience may trigger reactivation of existing memories. However, such reactivation may have different representational consequences within the hippocampal circuit. Computational theories of hippocampal function suggest that dentate gyrus and CA2,3 (DG/CA2,3) are biased to differentiate highly similar memories, whereas CA1 may integrate related events by representing them with overlapping neural codes. Here, we tested whether the formation of differentiated or integrated representations in hippocampal subfields depends on the strength of memory reactivation during learning. Human participants of both sexes learned associations (AB pairs, either face-shape or scene-shape), and then underwent fMRI scanning while they encoded overlapping associations (BC shape-object pairs). Both before and after learning, participants were also scanned while viewing indirectly related elements of the overlapping memories (A and C images) in isolation. We used multivariate pattern analyses to measure reactivation of initial pair memories (A items) during overlapping pair (BC) learning, as well as learning-related representational change for indirectly related memory elements in hippocampal subfields. When prior memories were strongly reactivated during overlapping pair encoding, DG/CA2,3 and subiculum representations for indirectly related images (A and C) became less similar, consistent with pattern differentiation. Simultaneously, memory reactivation during new learning promoted integration in CA1, where representations for indirectly related memory elements became more similar after learning. Furthermore, memory reactivation and subiculum representation predicted faster and more accurate inference (AC) decisions. These data show that reactivation of related memories during new learning leads to dissociable coding strategies in hippocampal subfields, in line with computational theories.SIGNIFICANCE STATEMENT The flexibility of episodic memory allows us to remember both the details that differentiate similar events and the commonalities among them. Here, we tested how reactivation of past experience during new learning promotes formation of neural representations that might serve these two memory functions. We found that memory reactivation during learning promoted formation of differentiated representations for overlapping memories in the dentate gyrus/CA2,3 and subiculum subfields of the hippocampus, while simultaneously leading to the formation of integrated representations of related events in subfield CA1 Furthermore, memory reactivation and subiculum representation predicted success when inferring indirect relationships among events. These findings indicate that memory reactivation is an important learning signal that influences how overlapping events are represented within the hippocampal circuit.

The Global Configuration of Visual Stimuli Alters Co-Fluctuations of Cross-Hemispheric Human Brain Activity.

We tested how a stimulus gestalt, defined by the neuronal interaction between local and global features of a stimulus, is represented within human primary visual cortex (V1). We used high-resolution fMRI, which serves as a surrogate of neuronal activation, to measure co-fluctuations within subregions of V1 as (male and female) subjects were presented with peripheral stimuli, each with different global configurations. We found stronger cross-hemisphere correlations when fine-scale V1 cortical subregions represented parts of the same object compared with different objects. This result was consistent with the vertical bias in global processing and, critically, was independent of the task and local discontinuities within objects. Thus, despite the relatively small receptive fields of neurons within V1, global stimulus configuration affects neuronal processing via correlated fluctuations between regions that represent different sectors of the visual field.SIGNIFICANCE STATEMENT We provide the first evidence for the impact of global stimulus configuration on cross-hemispheric fMRI fluctuations, measured in human primary visual cortex. Our results are consistent with changes in the level of γ-band synchrony, which has been shown to be affected by global stimulus configuration, being reflected in the level fMRI co-fluctuations. These data help narrow the gap between knowledge of global stimulus configuration encoding at the single-neuron level versus at the behavioral level.

Improved laminar specificity and sensitivity by combining SE and GE BOLD signals.

The most widely used gradient-echo (GE) blood oxygenation level-dependent (BOLD) contrast has high sensitivity, but low specificity due to draining vein contributions, while spin-echo (SE) BOLD approach at ultra-high magnetic fields is highly specific to neural active sites but has lower sensitivity. To obtain high specificity and sensitivity, we propose to utilize a vessel-size-sensitive filter to the GE-BOLD signal, which suppresses macrovascular contributions and to combine selectively retained microvascular GE-BOLD signals with the SE-BOLD signals. To investigate our proposed idea, fMRI with 0.8 mm isotropic resolution was performed on the primary motor and sensory cortices in humans at 7 T by implementing spin- and gradient-echo (SAGE) echo planar imaging (EPI) acquisition. Microvascular-passed sigmoidal filters were designed based upon the vessel-size-sensitive ΔR2*/ΔR2 value for retaining GE-BOLD signals originating from venous vessels with ≤ 45 µm and ≤ 65 µm diameter. Unlike GE-BOLD fMRI, the laminar profile of SAGE-BOLD fMRI with the vessel-size-sensitive filter peaked at ∼ 1.0 mm from the surface of the primary motor and sensory cortices, demonstrating an improvement of laminar specificity over GE-BOLD fMRI. Also, the functional sensitivity of SAGE BOLD at middle layers (0.75-1.5 mm) was improved by ∼ 80% to ∼100% when compared with SE BOLD. In summary, we showed that combined GE- and SE-BOLD fMRI with the vessel-size-sensitive filter indeed yielded improved laminar specificity and sensitivity and is therefore an excellent tool for high spatial resolution ultra-high filed (UHF)-fMRI studies for resolving mesoscopic functional units.

Critical factors in achieving fine-scale functional MRI: Removing sources of inadvertent spatial smoothing.

Ultra-high Field (≥7T) functional magnetic resonance imaging (UHF-fMRI) provides opportunities to resolve fine-scale features of functional architecture such as cerebral cortical columns and layers, in vivo. While the nominal resolution of modern fMRI acquisitions may appear to be sufficient to resolve these features, several common data preprocessing steps can introduce unwanted spatial blurring, especially those that require interpolation of the data. These resolution losses can impede the detection of the fine-scale features of interest. To examine quantitatively and systematically the sources of spatial resolution losses occurring during preprocessing, we used synthetic fMRI data and real fMRI data from the human visual cortex-the spatially interdigitated human V2 "thin" and "thick" stripes. The pattern of these cortical columns lies along the cortical surface and thus can be best appreciated using surface-based fMRI analysis. We used this as a testbed for evaluating strategies that can reduce spatial blurring of fMRI data. Our results show that resolution losses can be mitigated at multiple points in preprocessing pathway. We show that unwanted blur is introduced at each step of volume transformation and surface projection, and can be ameliorated by replacing multi-step transformations with equivalent single-step transformations. Surprisingly, the simple approaches of volume upsampling and of cortical mesh refinement also helped to reduce resolution losses caused by interpolation. Volume upsampling also serves to improve motion estimation accuracy, which helps to reduce blur. Moreover, we demonstrate that the level of spatial blurring is nonuniform over the brain-knowledge which is critical for interpreting data in high-resolution fMRI studies. Importantly, our study provides recommendations for reducing unwanted blurring during preprocessing as well as methods that enable quantitative comparisons between preprocessing strategies. These findings highlight several underappreciated sources of a spatial blur. Individually, the factors that contribute to spatial blur may appear to be minor, but in combination, the cumulative effects can hinder the interpretation of fine-scale fMRI and the detectability of these fine-scale features of functional architecture.

Ultrahigh Resolution fMRI at 7T Using Radial-Cartesian TURBINE Sampling.

PURPOSE: We investigate the use of TURBINE, a 3D radial-Cartesian acquisition scheme in which EPI planes are rotated about the phase-encoding axis to acquire a cylindrical k-space for high-fidelity ultrahigh isotropic resolution fMRI at 7 Tesla with minimal distortion and blurring. METHODS: An improved, completely self-navigated version of the TURBINE sampling scheme was designed for fMRI at 7 Telsa. To demonstrate the image quality and spatial specificity of the acquisition, thin-slab visual and motor BOLD fMRI at 0.67 mm isotropic resolution (16 mm slab, TRvol = 2.32 s), and 0.8 × 0.8 × 2.0 mm (whole-brain, TRvol = 2.4 s) data were acquired. To prioritize the high spatial fidelity, we employed a temporally regularized reconstruction to improve sensitivity without any spatial bias. RESULTS: TURBINE images provide high structural fidelity with almost no distortion, dropout, or T2 * blurring for the thin-slab acquisitions compared to conventional 3D EPI owing to the radial sampling in-plane and the short echo train used. This results in activation that can be localized to pre- and postcentral gyri in a motor task, for example, with excellent correspondence to brain structure measured by a T1 -MPRAGE. The benefits of TURBINE (low distortion, dropout, blurring) are reduced for the whole-brain acquisition due to the longer EPI train. We demonstrate robust BOLD activation at 0.67 mm isotropic resolution (thin-slab) and also anisotropic 0.8 × 0.8 × 2.0 mm (whole-brain) acquisitions. CONCLUSION: TURBINE is a promising acquisition approach for high-resolution, minimally distorted fMRI at 7 Tesla and could be particularly useful for fMRI in areas of high B0 inhomogeneity.

Population Receptive Field Characteristics in the between- and Within-Digit Dimensions of the Undominant Hand in the Primary Somatosensory Cortex.

Somatotopy is an important guiding principle for sensory fiber organization in the primary somatosensory cortex (S1), which reflects tactile information processing and is associated with disease-related reorganization. However, it is difficult to measure the neuronal encoding scheme in S1 in vivo in normal participants. Here, we investigated the somatotopic map of the undominant hand using a Bayesian population receptive field (pRF) model. The model was established in hand space with between- and within-digit dimensions. In the between-digit dimension, orderly representation was found, which had low variability across participants. The pRF shape tended to be elliptical for digits with high spatial acuity, for which the long axis was along the within-digit dimension. In addition, the pRF width showed different change trends in the 2 dimensions across digits. These results provide new insights into the neural mechanisms in S1, allowing for in-depth investigation of somatosensory information processing and disease-related reorganization.

Submillimeter fMRI reveals a layout of dorsal visual cortex in macaques, remarkably similar to New World monkeys.

The macaque dorsal occipital cortex is generally thought to contain an elongated third visual area, V3d, extending along most of the rostral border of area V2. In contrast, our submillimeter retinotopic fMRI maps (0.6-mm isotropic voxels, achieved by implanted phased-array receive coils) consistently show three sectors anterior to V2d. The dorsal (mirror image) sector complies with the traditional V3d definition, and the middle (nonmirror image) sector with V3A. The ventral (mirror image) sector bends away from V2d, as does the ventrolateral posterior area (VLP) in marmosets and the dorsolateral posterior area (DLP) in owl monkeys, and represents the entire contralateral hemifield as V3A does. Its population-receptive field size, however, suggests that this ventral sector is another area at the same hierarchical level as V4d. Hence, contrary to prevailing views, the retinotopic organization of cortex rostral to V2d differs substantially from widely accepted models. Instead, it is evolutionarily largely conserved in Old and New World monkeys given its surprisingly similar overall visuotopic organization.

Asymmetries in Global Perception Are Represented in Near- versus Far-Preferring Clusters in Human Visual Cortex.

Human perception is more "global" when stimuli are viewed within the lower (rather than the upper) visual field. This phenomenon is typically considered as a 2-D phenomenon, likely due to differential neural processing within dorsal versus ventral cortical areas that represent lower versus upper visual fields, respectively. Here we test a novel hypothesis that this vertical asymmetry in global processing is a 3-D phenomenon associated with (1) higher ecological relevance of low-spatial frequency (SF) components in encoding near (compared with far) visual objects and (2) the fact that near objects are more frequently found in lower rather than upper visual fields. Using high-resolution fMRI, collected within an ultra-high-field (7 T) scanner, we found that the extent of vertical asymmetry in global visual processing in human subjects (n = 10) was correlated with the fMRI response evoked by disparity-varying stimuli in human cortical area V3A. We also found that near-preferring clusters in V3A, located within stereoselective cortical columns, responded more selectively than far-preferring clusters, to low-SF features. These findings support the hypothesis that vertical asymmetry in global processing is a 3-D (not a 2-D) phenomenon, associated with the function of the stereoselective columns within visual cortex, especially those located within visual area V3A.SIGNIFICANCE STATEMENT Here we test and confirm a new hypothesis: fine-scale neural mechanisms underlying the vertical asymmetry in global visual processing. According to this hypothesis, the asymmetry in global visual processing is a 3-D (rather than a 2-D) phenomenon, reflected in the function of fine-scale cortical structures (clusters and columns) underlying depth perception. Our findings highlight the importance of considering these structures, as regions of interest, in clarifying the neural mechanisms underlying visual perception. The results also highlight the importance of statistics of natural scenes in shaping human visual perception.

Hippocampal Representation of Threat Features and Behavior in a Human Approach-Avoidance Conflict Anxiety Task.

Decisions under threat are crucial to survival and require integration of distinct situational features, such as threat probability and magnitude. Recent evidence from human lesion and neuroimaging studies implicated anterior hippocampus (aHC) and amygdala in approach-avoidance decisions under threat, and linked their integrity to cautious behavior. Here we sought to elucidate how threat dimensions and behavior are represented in these structures. Twenty human participants (11 female) completed an approach-avoidance conflict task during high-resolution fMRI. Participants could gather tokens under threat of capture by a virtual predator, which would lead to token loss. Threat probability (predator wake-up rate) and magnitude (amount of token loss) varied on each trial. To disentangle effects of threat features, and ensuing behavior, we performed a multifold parametric analysis. We found that high threat probability and magnitude related to BOLD signal in left aHC/entorhinal cortex. However, BOLD signal in this region was better explained by avoidance behavior than by these threat features. A priori ROI analysis confirmed the relation of aHC BOLD response with avoidance. Exploratory subfield analysis revealed that this relation was specific to anterior CA2/3 but not CA1. Left lateral amygdala responded to low and high, but not intermediate, threat probability. Our results suggest that aHC BOLD signal is better explained by avoidance behavior than by threat features in approach-avoidance conflict. Rather than representing threat features in a monotonic manner, it appears that aHC may compute approach-avoidance decisions based on integration of situational threat features represented in other neural structures.SIGNIFICANCE STATEMENT An effective threat anticipation system is crucial to survival across species. Natural threats, however, are diverse and have distinct features. To be able to adapt to different modes of danger, the brain needs to recognize these features, integrate them, and use them to modify behavior. Our results disclose the human anterior hippocampus as a likely arbiter of approach-avoidance decisions harnessing compound environmental information while partially replicating previous findings and blending into recent efforts to illuminate the neural basis of approach-avoidance conflict in humans.

Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning.

Whether it be in a single neuron or a more complex biological system like the human brain, form and function are often directly related. The functional organization of human visual cortex, for instance, is tightly coupled with the underlying anatomy with cortical shape having been shown to be a useful predictor of the retinotopic organization in early visual cortex. Although the current state-of-the-art in predicting retinotopic maps is able to account for gross individual differences, such models are unable to account for any idiosyncratic differences in the structure-function relationship from anatomical information alone due to their initial assumption of a template. Here we developed a geometric deep learning model capable of exploiting the actual structure of the cortex to learn the complex relationship between brain function and anatomy in human visual cortex such that more realistic and idiosyncratic maps could be predicted. We show that our neural network was not only able to predict the functional organization throughout the visual cortical hierarchy, but that it was also able to predict nuanced variations across individuals. Although we demonstrate its utility for modeling the relationship between structure and function in human visual cortex, our approach is flexible and well-suited for a range of other applications involving data structured in non-Euclidean spaces.

Feasibility of spiral fMRI based on an LTI gradient model.

Spiral imaging is very well suited for functional MRI, however its use has been limited by the fact that artifacts caused by gradient imperfections and B0 inhomogeneity are more difficult to correct compared to EPI. Effective correction requires accurate knowledge of the traversed k-space trajectory. With the goal of making spiral fMRI more accessible, we have evaluated image reconstruction using trajectories predicted by the gradient impulse response function (GIRF), which can be determined in a one-time calibration step. GIRF-predicted reconstruction was tested for high-resolution (0.8 mm) fMRI at 7T. Image quality and functional results of the reconstructions using GIRF-prediction were compared to reconstructions using the nominal trajectory and concurrent field monitoring. The reconstructions using nominal spiral trajectories contain substantial artifacts and the activation maps contain misplaced activation. Image artifacts are substantially reduced when using the GIRF-predicted reconstruction, and the activation maps for the GIRF-predicted and monitored reconstructions largely overlap. The GIRF reconstruction provides a large increase in the spatial specificity of the activation compared to the nominal reconstruction. The GIRF-reconstruction generates image quality and fMRI results similar to using a concurrently monitored trajectory. The presented approach does not prolong or complicate the fMRI acquisition. Using GIRF-predicted trajectories has the potential to enable high-quality spiral fMRI in situations where concurrent trajectory monitoring is not available.

Improvement of sensitivity and specificity for laminar BOLD fMRI with double spin-echo EPI in humans at 7 T.

Mapping mesoscopic cortical functional units such as columns or laminae is increasingly pursued by ultra-high field (UHF) functional magnetic resonance imaging (fMRI). The most popular approach for high-resolution fMRI is currently gradient-echo (GE) blood oxygenation level-dependent (BOLD) fMRI. However, its spatial accuracy is reduced due to its sensitivity to draining vessels, including pial veins, whereas spin-echo (SE) BOLD signal is expected to have higher spatial accuracy, albeit with lower sensitivity than the GE-BOLD signal. Here, we introduce a new double spin-echo (dSE) echo-planar imaging (EPI) method to improve the sensitivity of SE-BOLD contrast by averaging two spin-echoes using three radiofrequency pulses. Human fMRI experiments were performed with slices perpendicular to the central sulcus between motor and sensory cortices at 7 T during fist-clenching with touching. First, we evaluated the feasibility of single-shot dSE-EPI for BOLD fMRI with 1.5 mm isotropic resolution and found that dSE-BOLD fMRI has higher signal-to-noise ratio (SNR), temporal SNR (tSNR), and higher functional sensitivity than conventional SE-BOLD fMRI. Second, to investigate the laminar specificity of dSE-BOLD fMRI, we implemented a multi-shot approach to achieve 0.8-mm isotropic resolution with sliding-window reconstruction. Unlike GE-BOLD fMRI, the cortical profile of dSE-BOLD fMRI peaked at ~ 1.0 mm from the surface of the primary motor and sensory cortices, demonstrating an improvement of laminar specificity in humans over GE-BOLD fMRI. The proposed multi-shot dSE-EPI method is viable for high spatial resolution UHF-fMRI studies in the pursuit of resolving mesoscopic functional units.

Increased dynamic flexibility in the medial temporal lobe network following an exercise intervention mediates generalization of prior learning.

Recent work has conceptualized the brain as a network comprised of groups of sub-networks or modules. "Flexibility" of brain network(s) indexes the dynamic reconfiguration of comprising modules. Using novel techniques from dynamic network neuroscience applied to high-resolution resting-state functional magnetic resonance imaging (fMRI), the present study investigated the effects of an aerobic exercise intervention on the dynamic rearrangement of modular community structure-a measure of neural flexibility-within the medial temporal lobe (MTL) network. The MTL is one of the earliest brain regions impacted by Alzheimer's disease. It is also a major site of neuroplasticity that is sensitive to the effects of exercise. In a two-group non-randomized, repeated measures and matched control design with 34 healthy older adults, we observed an exercise-related increase in flexibility within the MTL network. Furthermore, MTL network flexibility mediated the beneficial effect aerobic exercise had on mnemonic flexibility, as measured by the ability to generalize past learning to novel task demands. Our results suggest that exercise exerts a rehabilitative and protective effect on MTL function, resulting in dynamically evolving networks of regions that interact in complex communication patterns. These reconfigurations may underlie exercise-induced improvements on cognitive measures of generalization, which are sensitive to subtle changes in the MTL.

Integrated VASO and perfusion contrast: A new tool for laminar functional MRI.

Earlier research in cats has shown that both cerebral blood volume (CBV) and cerebral blood flow (CBF) can be used to identify layer-dependent fMRI activation with spatial specificity superior to gradient-echo blood-oxygen-level-dependent (BOLD) contrast (Jin and Kim, 2008a). CBF contrast of perfusion fMRI at ultra-high field has not been widely applied in humans to measure laminar activity due to its low sensitivity, while CBV contrast for fMRI using vascular space occupancy (VASO) has been successfully used. However, VASO can be compromised by interference of blood in-flow effects and a temporally limited acquisition window around the blood-nulling time point. Here, we proposed to use DANTE (Delay Alternating with Nutation for Tailored Excitation) pulse trains combined with 3D-EPI to acquire an integrated VASO and perfusion (VAPER) contrast. The signal origin of the VAPER contrast was theoretically evaluated with respect to its CBV and CBF contributions using a four-compartment simulation model. The feasibility of VAPER to measure layer-dependent activity was empirically investigated in human primary motor cortex at 7 â€‹T. We demonstrated this new tool, with its highly specified functional layer profile, robust reproducibility, and improved sensitivity, to allow investigation of layer-specific cortical functions.

Mapping subcomponents of numerical cognition in relation to functional and anatomical landmarks of human parietal cortex.

Human functional imaging has identified the middle part of the intraparietal sulcus (IPS) as an important brain substrate for different types of numerical tasks. This area is often equated with the macaque ventral intraparietal area (VIP) where neuronal selectivity for non-symbolic numerical stimuli (sets of items) is found. However, the low spatial resolution and whole-brain averaging analysis performed in most fMRI studies limit the extent to which an exact correspondence of activations in different numerical tasks with specific sub-regions of the IPS can be established. Here we acquired high-resolution 7T fMRI data in a group of human adults and related the activations in several numerical contrasts (implying different numerical stimuli and tasks) to anatomical and functional landmarks on the cortical surface. Our results reveal a functional heterogeneity within human intraparietal cortex where the retinotopic visual field maps in superior/medial parts of the IPS and superior parietal gyrus respond preferentially to the visual processing of concrete sets of items (over single Arabic numerals), whereas lateral/inferior parts of the IPS are predominantly recruited during numerical operations such as calculation and quantitative comparison. Since calculation and comparison-related activity fell mainly outside the retinotopic visual field maps considered the human functional equivalent of the monkey VIP/LIP complex, the areas most activated during such numerical operations in humans are likely different from VIP.

A critical assessment of data quality and venous effects in sub-millimeter fMRI.

Advances in hardware, pulse sequences, and reconstruction techniques have made it possible to perform functional magnetic resonance imaging (fMRI) at sub-millimeter resolution while maintaining high spatial coverage and acceptable signal-to-noise ratio. Here, we examine whether sub-millimeter fMRI can be used as a routine method for obtaining accurate measurements of fine-scale local neural activity. We conducted fMRI in human visual cortex during a simple event-related visual experiment (7 T, gradient-echo EPI, 0.8-mm isotropic voxels, 2.2-s sampling rate, 84 slices), and developed analysis and visualization tools to assess the quality of the data. Our results fall along three lines of inquiry. First, we find that the acquired fMRI images, combined with appropriate surface-based processing, provide reliable and accurate measurements of fine-scale blood oxygenation level dependent (BOLD) activity patterns. Second, we show that the highly folded structure of cortex causes substantial biases on spatial resolution and data visualization. Third, we examine the well-recognized issue of venous contributions to fMRI signals. In a systematic assessment of large sections of cortex measured at a fine scale, we show that time-averaged T2*-weighted EPI intensity is a simple, robust marker of venous effects. These venous effects are unevenly distributed across cortex, are more pronounced in gyri and outer cortical depths, and are, to a certain degree, in consistent locations across subjects relative to cortical folding. Furthermore, we show that these venous effects are strongly correlated with BOLD responses evoked by the experiment. We conclude that sub-millimeter fMRI can provide robust information about fine-scale BOLD activity patterns, but special care must be exercised in visualizing and interpreting these patterns, especially with regards to the confounding influence of the brain's vasculature. To help translate these methodological findings to neuroscience research, we provide practical suggestions for both high-resolution and standard-resolution fMRI studies.