High-resolution fMRI at 7 Tesla: challenges, promises and recent developments for individual-focused fMRI studies

Flexible metasurface for improving brain imaging at 7T

PURPOSE

Ultra-high field MRI offers unprecedented detail for noninvasive visualization of the human brain. However, brain imaging is challenging at 7T due to the B  1 + $$ {}_1^{+} $$ field inhomogeneity, which results in signal intensity drops in temporal lobes and a bright region in the brain center. This study aims to evaluate using a metasurface to improve brain imaging at 7T and simplify the investigative workflow.

METHODS

Two flexible metasurfaces comprising a periodic structure of copper strips and parallel-plate capacitive elements printed on an ultra-thin substrate were optimized for brain imaging and implemented via PCB. We considered two setups: (1) two metasurfaces located near the temporal lobes and (2) one metasurface placed near the occipital lobe. The effect of metasurface placement on the transmit efficiency and specific absorption rate was evaluated via electromagnetic simulation studies with voxelized models. In addition, their impact on signal-to-noise ratio (SNR) and diagnostic image quality was assessed in vivo for two male and one female volunteers.

RESULTS

Placement of metasurfaces near the regions of interest led to an increase in homogeneity of the transmit field by 5% and 10.5% in the right temporal lobe and occipital lobe for a male subject, respectively. SAR efficiency values changed insignificantly, dropping by less than 8% for all investigated setups. In vivo studies also confirmed the numerically predicted improvement in field distribution and receive sensitivity in the desired ROI.

CONCLUSION

Optimized metasurfaces enable homogenizing transmit field distribution in the brain at 7T. The proposed lightweight and flexible structure can potentially provide MR examination with higher diagnostic value images.

Prefrontal cortex neurons encode ambient light intensity differentially across regions and layers

While light can affect emotional and cognitive processes of the medial prefrontal cortex (mPFC), no light-encoding was hitherto identified in this region. Here, extracellular recordings in awake mice revealed that over half of studied mPFC neurons showed photosensitivity, that was diminished by inhibition of intrinsically photosensitive retinal ganglion cells (ipRGCs), or of the upstream thalamic perihabenular nucleus (PHb). In 15% of mPFC photosensitive neurons, firing rate changed monotonically along light-intensity steps and gradients. These light-intensity-encoding neurons comprised four types, two enhancing and two suppressing their firing rate with increased light intensity. Similar types were identified in the PHb, where they exhibited shorter latency and increased sensitivity. Light suppressed prelimbic activity but boosted infralimbic activity, mirroring the regions' contrasting roles in fear-conditioning, drug-seeking, and anxiety. We posit that prefrontal photosensitivity represents a substrate of light-susceptible, mPFC-mediated functions, which could be ultimately studied as a therapeutical target in psychiatric and addiction disorders.

Within-subject reliability of brain networks during advanced meditation: An intensively sampled 7 Tesla MRI case study

Advanced meditation such as jhana meditation can produce various altered states of consciousness (jhanas) and cultivate rewarding psychological qualities including joy, peace, compassion, and attentional stability. Mapping the neurobiological substrates of jhana meditation can inform the development and application of advanced meditation to enhance well-being. Only two prior studies have attempted to investigate the neural correlates of jhana meditation, and the rarity of adept practitioners has largely restricted the size and extent of these studies. Therefore, examining the consistency and reliability of observed brain responses associated with jhana meditation can be valuable. In this study, we aimed to characterize functional magnetic resonance imaging (fMRI) reliability within a single subject over repeated runs in canonical brain networks during jhana meditation performed by an adept practitioner over 5 days (27 fMRI runs) inside an ultra-high field 7 Tesla MRI scanner. We found that thalamus and several cortical networks, that is, the somatomotor, limbic, default-mode, control, and temporo-parietal, demonstrated good within-subject reliability across all jhanas. Additionally, we found that several other relevant brain networks (e.g., attention, salience) showed noticeable increases in reliability when fMRI measurements were adjusted for variability in self-reported phenomenology related to jhana meditation. Overall, we present a preliminary template of reliable brain areas likely underpinning core neurocognitive elements of jhana meditation, and highlight the utility of neurophenomenological experimental designs for better characterizing neuronal variability associated with advanced meditative states.

The temporal specificity of BOLD fMRI is systematically related to anatomical and vascular features of the human brain

The ability to detect fast responses with functional MRI depends on the speed of hemodynamic responses to neural activity, because hemodynamic responses act as a temporal low-pass filter smoothing out rapid changes. However, hemodynamic responses (their shape and timing) are highly variable across the brain and across stimuli. This heterogeneity of responses implies that the temporal specificity of fMRI signals, or the ability of fMRI to preserve fast information, should also vary substantially across the cortex. In this work we investigated how local differences in hemodynamic response timing impact the temporal specificity of fMRI. We conducted our research using ultra-high field (7T) fMRI at high spatiotemporal resolution, using the primary visual cortex (V1) as a model area for investigation. We used visual stimuli oscillating at slow and fast frequencies to probe the temporal specificity of individual voxels. As expected, we identified substantial variability in temporal specificity, with some voxels preserving their responses to fast neural activity more effectively than others. We investigated which voxels had the highest temporal specificity and related those to anatomical and vascular features of V1. We found that low temporal specificity is only weakly explained by the presence of large veins or cerebral cortical depth. Notably, however, temporal specificity depended strongly on a voxel's position along the anterior-posterior anatomical axis of V1, with voxels within the calcarine sulcus being capable of preserving close to 25% of their amplitude as the frequency of stimulation increased from 0.05-Hz to 0.20-Hz, and voxels nearest to the occipital pole preserving less than 18%. These results indicate that detection biases in high-resolution fMRI will depend on the anatomical and vascular features of the area being imaged, and that these biases will differ depending on the timing of the underlying neuronal activity. Importantly, this spatial heterogeneity of temporal specificity suggests that it could be exploited to achieve higher specificity in some locations, and that tailored data analysis strategies may help improve the detection and interpretation of fast fMRI responses.

Meditation attenuates default-mode activity: A pilot study using ultra-high field 7 Tesla MRI

OBJECTIVES

Mapping the neurobiology of meditation has been bolstered by functional MRI (fMRI) research, with advancements in ultra-high field 7 Tesla fMRI further enhancing signal quality and neuroanatomical resolution. Here, we utilize 7 Tesla fMRI to examine the neural substrates of meditation and replicate existing widespread findings, after accounting for relevant physiological confounds.

METHODS

In this feasibility study, we scanned 10 beginner meditators (N = 10) while they either attended to breathing (focused attention meditation) or engaged in restful thinking (non-focused rest). We also measured and adjusted the fMRI signal for key physiological differences between meditation and rest. Finally, we explored changes in state mindfulness, state anxiety and focused attention attributes for up to 2 weeks following the single fMRI meditation session.

RESULTS

Group-level task fMRI analyses revealed significant reductions in activity during meditation relative to rest in default-mode network hubs, i.e., antero-medial prefrontal and posterior cingulate cortices, precuneus, as well as visual and thalamic regions. These findings survived stringent statistical corrections for fluctuations in physiological responses which demonstrated significant differences (p < 0.05/n, Bonferroni controlled) between meditation and rest. Compared to baseline, State Mindfulness Scale (SMS) scores were significantly elevated (F(3,9) = 8.16, p < 0.05/n, Bonferroni controlled) following the fMRI meditation session, and were closely maintained at 2-week follow up.

CONCLUSIONS

This pilot study establishes the feasibility and utility of investigating focused attention meditation using ultra-high field (7 Tesla) fMRI, by supporting widespread evidence that focused attention meditation attenuates default-mode activity responsible for self-referential processing. Future functional neuroimaging studies of meditation should control for physiological confounds and include behavioural assessments.

Physiological modeling of the BOLD signal and implications for effective connectivity: A primer

In this primer, I provide an overview of the physiological processes that contribute to the observed BOLD signal (i.e., the generative biophysical model), including their time course properties within the framework of the physiologically-informed dynamic causal modeling (P-DCM). The BOLD signal is primarily determined by the change in paramagnetic deoxygenated hemoglobin, which results from combination of changes in oxygen metabolism, and cerebral blood flow and volume. Specifically, the physiological origin of the so-called BOLD signal "transients" will be discussed, including the initial overshoot, steady-state activation and the post-stimulus undershoot. I argue that incorrect physiological assumptions in the generative model of the BOLD signal can lead to incorrect inferences pertaining to both local neuronal activity and effective connectivity between brain regions. In addition, I introduce the recent laminar BOLD signal model, which extends P-DCM to cortical depths-resolved BOLD signals, allowing for laminar neuronal activity to be determined using high-resolution fMRI data.

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed 'variants' in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability.

[Structural and functional characteristics of the brain and their role in the development of eating behaviour in obesity: A review]

Obesity is a major public health problem that requires new approaches. Despite all interventions, the behavioural and therapeutic interventions developed have demonstrated limited effectiveness in curbing the obesity epidemic. Findings from imaging studies of the brain suggest the existence of neural vulnerabilities and structural changes that are associated with the development of obesity and eating disorders. This review highlights the clinical relevance of brain neuroimaging research in obese individuals to prevent risky behaviour, early diagnosis, and the development of new safer and more effective treatments.

Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders

Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain's electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.

Imaging the columnar functional organization of human area MT+ to axis-of-motion stimuli using VASO at 7 Tesla

Cortical columns of direction-selective neurons in the motion sensitive area (MT) have been successfully established as a microscopic feature of the neocortex in animals. The same property has been investigated at mesoscale (<1 mm) in the homologous brain area (hMT+, V5) in living humans by using ultra-high field functional magnetic resonance imaging (fMRI). Despite the reproducibility of the selective response to axis-of-motion stimuli, clear quantitative evidence for the columnar organization of hMT+ is still lacking. Using cerebral blood volume (CBV)-sensitive fMRI at 7 Tesla with submillimeter resolution and high spatial specificity to microvasculature, we investigate the columnar functional organization of hMT+ in 5 participants perceiving axis-of-motion stimuli for both blood oxygenation level dependent (BOLD) and vascular space occupancy (VASO) contrast mechanisms provided by the used slice-selective slab-inversion (SS-SI)-VASO sequence. With the development of a new searchlight algorithm for column detection, we provide the first quantitative columnarity map that characterizes the entire 3D hMT+ volume. Using voxel-wise measures of sensitivity and specificity, we demonstrate the advantage of using CBV-sensitive fMRI to detect mesoscopic cortical features by revealing higher specificity of axis-of-motion cortical columns for VASO as compared to BOLD contrast. These voxel-wise metrics also provide further insights on how to mitigate the highly debated draining veins effect. We conclude that using CBV-VASO fMRI together with voxel-wise measurements of sensitivity, specificity and columnarity offers a promising avenue to quantify the mesoscopic organization of hMT+ with respect to axis-of-motion stimuli. Furthermore, our approach and methodological developments are generalizable and applicable to other human brain areas where similar mesoscopic research questions are addressed.

White Matter Cerebrovascular Reactivity: Effects of Microangiopathy and Proximal Occlusions on the Dynamic BOLD Response

Introduction

Cerebral microangiopathy often manifests as white matter hyperintensities (WMH) on T2-weighted MR images and is associated with elevated stroke risk. Large vessel steno-occlusive disease (SOD) is also independently associated with stroke risk, however, the interaction of microangiopathy and SOD is not well understood. Cerebrovascular reactivity (CVR) describes the capacity of cerebral circulation to adapt to changes in perfusion pressure and neurovascular demand, and its impairment portends future infarctions. CVR can be measured with blood oxygen level dependent (BOLD) imaging following acetazolamide stimulus (ACZ-BOLD). We studied CVR differences between WMH and normal-appearing white matter (NAWM) in patients with chronic SOD, hypothesizing additive influences upon CVR measured by novel, fully dynamic CVR maxima ( CVR _max ).

Methods

A cross sectional study was conducted to measure per-voxel, per-TR maximal CVR ( CVR _max ) using a custom computational pipeline in 23 subjects with angiographically-proven unilateral SOD. WMH and NAWM masks were applied to CVR _max maps. White matter was subclassified with respect to the SOD-affected hemisphere, including: i. contralateral NAWM; ii. contralateral WMH iii. ipsilateral NAWM; iv. ipsilateral WMH. CVR _max was compared between these groups with a Kruskal-Wallis test followed by a Dunn-Sidak post-hoc test for multiple comparisons.

Results

19 subjects (age 50±12 years, 53% female) undergoing 25 examinations met criteria. WMH volume was asymmetric in 16/19 subjects with 13/16 exhibiting higher volumes ipsilateral to SOD. Pairwise comparisons of CVR _max between groups was significant with ipsilateral WMH CVR _max lower than contralateral NAWM (p=0.015) and contralateral WMH (p=0.003) when comparing in-subject medians and lower than all groups when comparing pooled voxelwise values across all subjects (p<0.0001). No significant relationship between WMH lesion size and CVR _max was detected.

Conclusion

Our results suggest additive effects of microvascular and macrovascular disease upon white matter CVR, but with greater overall effects relating to macrovascular SOD than to apparent microangiopathy. Dynamic ACZ-BOLD presents a promising path towards a quantitative stroke risk imaging biomarker.

BACKGROUND

Cerebral white matter (WM) microangiopathy manifests as sporadic or sometimes confluent high intensity lesions in MR imaging with T2-weighting, and bears known associations with stroke, cognitive disability, depression and other neurological disorders ^1-5 . Deep white matter is particularly susceptible to ischemic injury owing to the deprivation of collateral flow between penetrating arterial territories, and hence deep white matter hyperintensities (WMH) may portend future infarctions ^6-8 . The pathophysiology of WMH is variable but commonly includes a cascade of microvascular lipohyalinosis and atherosclerosis together with impaired vascular endothelial and neurogliovascular integrity, leading to blood brain barrier dysfunction, interstitial fluid accumulation, and eventually tissue damage ^9-14 . Independent of the microcirculation, cervical and intracranial large vessel steno-occlusive disease (SOD) often results from atheromatous disease and is associated with increased risk of stroke owing to thromboembolic phenomena, hypoperfusion, or combinations thereof ^15-17 . White matter disease is more common in the affected hemisphere of patients with asymmetric or unilateral SOD, producing both macroscopic WMH detectable by routine structural MRI, as well as microstructural changes and altered structural connectivity detected by advanced diffusion microstructural imaging ^{18, 19} . An improved understanding of the interaction of microvascular disease (i.e., WMH) and macrovascular steno-occlusion could better inform stroke risk stratification and guide treatment strategies when coexistent. Cerebrovascular reactivity (CVR) is an autoregulatory adaptation characterized by the capacity of the cerebral circulation to respond to physiological or pharmacological vasodilatory stimuli ^20-22 . CVR may be heterogeneous and varies across tissue type and pathological states ^{1, 16} . Alterations in CVR are associated with elevated stroke risk in SOD patients, although white matter CVR, and in particular the CVR profiles of WMH, are only sparsely studied and not fully understood ^{1, 23-26} . We have previously employed blood oxygen level dependent (BOLD) imaging following a hemodynamic stimulus with acetazolamide (ACZ) in order to measure CVR (i.e. ACZ-BOLD) ^{21, 27, 28} . Despite the emergence of ACZ-BOLD as a technique for clinical and experimental use, poor signal-to-noise characteristics of the BOLD effect have generally limited its interpretation to coarse, time-averaged assessment of the terminal ACZ response at arbitrarily prescribed delays following ACZ administration (e.g. 10-20 minutes) ²⁹ . More recently, we have introduced a dedicated computational pipeline to overcome historically intractable signal-to-noise ratio (SNR) limitations of BOLD, enabling fully dynamic characterization of the cerebrovascular response, including identification of previously unreported, unsustained or transient CVR maxima ( CVR _max ) following hemodynamic provocation ^{27, 30} . In this study, we compared such dynamic interrogation of true CVR maxima between WMH and normal appearing white matter (NAWM) among patients with chronic, unilateral SOD in order to quantify their interaction and to assess the hypothesized additive effects of angiographically-evident macrovascular stenoses when intersecting microangiopathic WMH.

Improving detection of fMRI activation at 1.5 T using high permittivity ceramics

In this work, we propose an application of high permittivity materials (HPMs) to improve functional magnetic resonance imaging (fMRI) at 1.5 T, increasing the receive (Rx) sensitivity of a commercial multi-channel head coil. To evaluate the transmit efficiency, specific absorption rate (SAR), and the signal-to-noise ratio (SNR) changes introduced by the HPMs with relative permittivity of 4500, we considered the following configurations in simulation: a whole-body birdcage coil and an Rx-only multi-channel head coil with and without the HPM blocks in the presence of a homogeneous head phantom or a human body model. Experimental studies were also performed with a phantom and with volunteers. Seven healthy volunteers enrolled in a prospective study of fMRI activation in the motor cortex with and without HPMs. fMRI data were analyzed using group-level paired T-tests between acquisitions with and without HPM blocks. Both electromagnetic simulations and experimental measurements showed ∼25% improvement in the Rx sensitivity of a commercial head coil in the areas of interest when HPM blocks were placed in close proximity. It increased the detected motor cortex fMRI activation volume by an average of 56%, thus resulting in more sensitive functional imaging at 1.5 T.

Review of data processing of functional optical microscopy for neuroscience

Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.

The influence of the subcortex and brain stem on overeating: How advances in functional neuroimaging can be applied to expand neurobiological models to beyond the cortex

Functional neuroimaging has become a widely used tool in obesity and eating disorder research to explore the alterations in neurobiology that underlie overeating and binge eating behaviors. Current and traditional neurobiological models underscore the importance of impairments in brain systems supporting reward, cognitive control, attention, and emotion regulation as primary drivers for overeating. Due to the technical limitations of standard field strength functional magnetic resonance imaging (fMRI) scanners, human neuroimaging research to date has focused largely on cortical and basal ganglia effects on appetitive behaviors. The present review draws on animal and human research to highlight how neural signaling encoding energy regulation, reward-learning, and habit formation converge on hypothalamic, brainstem, thalamic, and striatal regions to contribute to overeating in humans. We also consider the role of regions such as the mediodorsal thalamus, ventral striatum, lateral hypothalamus and locus coeruleus in supporting habit formation, inhibitory control of food craving, and attentional biases. Through these discussions, we present proposals on how the neurobiology underlying these processes could be examined using functional neuroimaging and highlight how ultra-high field 7-Tesla (7 T) fMRI may be leveraged to elucidate the potential functional alterations in subcortical networks. Focus is given to how interactions of these regions with peripheral endocannabinoids and neuropeptides, such as orexin, could be explored. Technical and methodological aspects regarding the use of ultra-high field 7 T fMRI to study eating behaviors are also reviewed.

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.

New avenues for functional neuroimaging: ultra-high field MRI and OPM-MEG

Functional brain imaging technology has developed rapidly in recent years. On the one hand, high-field 7-Tesla magnetic resonance imaging (MRI) has excelled the limited spatial resolution of 3-Tesla MRI, allowing us to enter a new world of mesoscopic imaging from the macroscopic imaging of human brain functions. On the other hand, novel optical pumping magnetometer-magnetoencephalography (OPM-MEG) has broken down the technical barriers of traditional superconducting MEG, which brings imaging of neuronal electromagnetic signals from cortical imaging to whole-brain imaging. This article aims to present a brief introduction regarding the development of conventional MRI and MEG technology, and, more importantly, to delineate that high-field MRI and OPM-MEG complement each other and together will lead us into a new era of functional brain imaging.

Divisive normalization unifies disparate response signatures throughout the human visual hierarchy

A canonical neural computation is a mathematical operation applied by the brain in a wide variety of contexts and capable of explaining and unifying seemingly unrelated neural and perceptual phenomena. Here, we use a combination of state-of-the-art experiments (ultra-high-field functional MRI) and mathematical methods (population receptive field [pRF] modeling) to uniquely demonstrate the role of divisive normalization (DN) as the canonical neural computation underlying visuospatial responses throughout the human visual hierarchy. The DN pRF model provides a tool to investigate and interpret the computational processes underlying neural responses in human and animal recordings, but also in clinical and cognitive dimensions.

Neural processing is hypothesized to apply the same mathematical operations in a variety of contexts, implementing so-called canonical neural computations. Divisive normalization (DN) is considered a prime candidate for a canonical computation. Here, we propose a population receptive field (pRF) model based on DN and evaluate it using ultra-high-field functional MRI (fMRI). The DN model parsimoniously captures seemingly disparate response signatures with a single computation, superseding existing pRF models in both performance and biological plausibility. We observe systematic variations in specific DN model parameters across the visual hierarchy and show how they relate to differences in response modulation and visuospatial information integration. The DN model delivers a unifying framework for visuospatial responses throughout the human visual hierarchy and provides insights into its underlying information-encoding computations. These findings extend the role of DN as a canonical computation to neuronal populations throughout the human visual hierarchy.

Individualized cognitive neuroscience needs 7T: Comparing numerosity maps at 3T and 7T MRI

The field of cognitive neuroscience is weighing evidence about whether to move from the current standard field strength of 3 Tesla (3T) to ultra-high field (UHF) of 7T and above. The present study contributes to the evidence by comparing a computational cognitive neuroscience paradigm at 3T and 7T. The goal was to evaluate the practical effects, i.e. model predictive power, of field strength on a numerosity task using accessible pre-processing and analysis tools. Previously, using 7T functional magnetic resonance imaging and biologically-inspired analyses, i.e. population receptive field modelling, we discovered topographical organization of numerosity-selective neural populations in human parietal cortex. Here we show that these topographic maps are also detectable at 3T. However, averaging of many more functional runs was required at 3T to reliably reconstruct numerosity maps. On average, one 7T run had about four times the model predictive power of one 3T run. We believe that this amount of scanning would have made the initial discovery of the numerosity maps on 3T highly infeasible in practice. Therefore, we suggest that the higher signal-to-noise ratio and signal sensitivity of UHF MRI is necessary to build mechanistic models of the organization and function of our cognitive abilities in individual participants.