Intracortical depth analyses of frequency-sensitive regions of human auditory cortex using 7TfMRI

Individual connectivity-based parcellations reflect functional properties of human auditory cortex

Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of auditory cortex. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.

Neuronal Modeling of Cross-Sensory Visual Evoked Magnetoencephalography Responses in the Auditory Cortex

Previous studies have demonstrated that auditory cortex activity can be influenced by cross-sensory visual inputs. Intracortical laminar recordings in nonhuman primates have suggested a feedforward (FF) type profile for auditory evoked but feedback (FB) type for visual evoked activity in the auditory cortex. To test whether cross-sensory visual evoked activity in the auditory cortex is associated with FB inputs also in humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex regions of interest, auditory evoked response showed peaks at 37 and 90 ms and visual evoked response at 125 ms. The inputs to the auditory cortex were modeled through FF- and FB-type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which links cellular- and circuit-level mechanisms to MEG signals. HNN modeling suggested that the experimentally observed auditory response could be explained by an FF input followed by an FB input, whereas the cross-sensory visual response could be adequately explained by just an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.

Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli

Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) have suggested a bottom-up feedforward (FF) type laminar profile for auditory evoked but top-down feedback (FB) type for cross-sensory visual evoked activity in the auditory cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex region of interest, auditory evoked responses showed peaks at 37 and 90 ms and cross-sensory visual responses at 125 ms. The inputs to the auditory cortex were then modeled through FF and FB type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which consists of a neocortical circuit model linking the cellular- and circuit-level mechanisms to MEG. The HNN models suggested that the measured auditory response could be explained by an FF input followed by an FB input, and the crosssensory visual response by an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG/EEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.

Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC

Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise - the dominant contributing noise component in high resolution fMRI. NORDIC PCA is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. As investigating auditory functional responses poses unique challenges, we anticipated that the benefit of this technique would be especially pronounced. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we also observed a reduction in the average response amplitude (percent signal), which may suggest that a small amount of signal was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.

Role of articulatory motor networks in perceptual categorization of speech signals: a 7T fMRI study

Speech and language processing involve complex interactions between cortical areas necessary for articulatory movements and auditory perception and a range of areas through which these are connected and interact. Despite their fundamental importance, the precise mechanisms underlying these processes are not fully elucidated. We measured BOLD signals from normal hearing participants using high-field 7 Tesla fMRI with 1-mm isotropic voxel resolution. The subjects performed 2 speech perception tasks (discrimination and classification) and a speech production task during the scan. By employing univariate and multivariate pattern analyses, we identified the neural signatures associated with speech production and perception. The left precentral, premotor, and inferior frontal cortex regions showed significant activations that correlated with phoneme category variability during perceptual discrimination tasks. In addition, the perceived sound categories could be decoded from signals in a region of interest defined based on activation related to production task. The results support the hypothesis that articulatory motor networks in the left hemisphere, typically associated with speech production, may also play a critical role in the perceptual categorization of syllables. The study provides valuable insights into the intricate neural mechanisms that underlie speech processing.

Role of Articulatory Motor Networks in Perceptual Categorization of Speech Signals: A 7 T fMRI Study

BACKGROUND

The association between brain regions involved in speech production and those that play a role in speech perception is not yet fully understood. We compared speech production related brain activity with activations resulting from perceptual categorization of syllables using high field 7 Tesla functional magnetic resonance imaging (fMRI) at 1-mm isotropic voxel resolution, enabling high localization accuracy compared to previous studies.

METHODS

Blood oxygenation level dependent (BOLD) signals were obtained in 20 normal hearing subjects using a simultaneous multi-slice (SMS) 7T echo-planar imaging (EPI) acquisition with whole-head coverage and 1 mm isotropic resolution. In a speech production localizer task, subjects were asked to produce a silent lip-round vowel /u/ in response to the visual cue "U" or purse their lips when they saw the cue "P". In a phoneme discrimination task, subjects were presented with pairs of syllables, which were equiprobably identical or different along an 8-step continuum between the prototypic /ba/ and /da/ sounds. After the presentation of each stimulus pair, the subjects were asked to indicate whether the two syllables they heard were identical or different by pressing one of two buttons. In a phoneme classification task, the subjects heard only one syllable and asked to indicate whether it was /ba/ or /da/.

RESULTS

Univariate fMRI analyses using a parametric modulation approach suggested that left motor, premotor, and frontal cortex BOLD activations correlate with phoneme category variability in the /ba/-/da/ discrimination task. In contrast, the variability related to acoustic features of the phonemes were the highest in the right primary auditory cortex. Our multivariate pattern analysis (MVPA) suggested that left precentral/inferior frontal cortex areas, which were associated with speech production according to the localizer task, play a role also in perceptual categorization of the syllables.

CONCLUSIONS

The results support the hypothesis that articulatory motor networks in the left hemisphere that are activated during speech production could also have a role in perceptual categorization of syllables. Importantly, high voxel-resolution combined with advanced coil technology allowed us to pinpoint the exact brain regions involved in both perception and production tasks.

Cerebral blood volume sensitive layer-fMRI in the human auditory cortex at 7T: Challenges and capabilities

The development of ultra high field fMRI signal readout strategies and contrasts has led to the possibility of imaging the human brain in vivo and non-invasively at increasingly higher spatial resolutions of cortical layers and columns. One emergent layer-fMRI acquisition method with increasing popularity is the cerebral blood volume sensitive sequence named vascular space occupancy (VASO). This approach has been shown to be mostly sensitive to locally-specific changes of laminar microvasculature, without unwanted biases of trans-laminar draining veins. Until now, however, VASO has not been applied in the technically challenging cortical area of the auditory cortex. Here, we describe the main challenges we encountered when developing a VASO protocol for auditory neuroscientific applications and the solutions we have adopted. With the resulting protocol, we present preliminary results of laminar responses to sounds and as a proof of concept for future investigations, we map the topographic representation of frequency preference (tonotopy) in the auditory 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.

Fast cortical surface reconstruction from MRI using deep learning

Reconstructing cortical surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based functional and anatomical image analyses. Conventional algorithms for cortical surface reconstruction are computationally inefficient and typically take several hours for each subject, causing a bottleneck in applications when a fast turnaround time is needed. To address this challenge, we propose a fast cortical surface reconstruction (FastCSR) pipeline by leveraging deep machine learning. We trained our model to learn an implicit representation of the cortical surface in volumetric space, termed the “level set representation”. A fast volumetric topology correction method and a topology-preserving surface mesh extraction procedure were employed to reconstruct the cortical surface based on the level set representation. Using 1-mm isotropic T1-weighted images, the FastCSR pipeline was able to reconstruct a subject’s cortical surfaces within 5 min with comparable surface quality, which is approximately 47 times faster than the traditional FreeSurfer pipeline. The advantage of FastCSR becomes even more apparent when processing high-resolution images. Importantly, the model demonstrated good generalizability in previously unseen data and showed high test–retest reliability in cortical morphometrics and anatomical parcellations. Finally, FastCSR was robust to images with compromised quality or with distortions caused by lesions. This fast and robust pipeline for cortical surface reconstruction may facilitate large-scale neuroimaging studies and has potential in clinical applications wherein brain images may be compromised.

Using high spatial resolution fMRI to understand representation in the auditory network

Following rapid methodological advances, ultra-high field (UHF) functional and anatomical magnetic resonance imaging (MRI) has been repeatedly and successfully used for the investigation of the human auditory system in recent years. Here, we review this work and argue that UHF MRI is uniquely suited to shed light on how sounds are represented throughout the network of auditory brain regions. That is, the provided gain in spatial resolution at UHF can be used to study the functional role of the small subcortical auditory processing stages and details of cortical processing. Further, by combining high spatial resolution with the versatility of MRI contrasts, UHF MRI has the potential to localize the primary auditory cortex in individual hemispheres. This is a prerequisite to study how sound representation in higher-level auditory cortex evolves from that in early (primary) auditory cortex. Finally, the access to independent signals across auditory cortical depths, as afforded by UHF, may reveal the computations that underlie the emergence of an abstract, categorical sound representation based on low-level acoustic feature processing. Efforts on these research topics are underway. Here we discuss promises as well as challenges that come with studying these research questions using UHF MRI, and provide a future outlook.

Brainwide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI

The hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies have observed similar resting-state functional connectivity (FC) profiles between aHPSFs. Alternatively, data-driven approaches investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may result in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7 T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2 s and brain-wide coverage to (1) investigate the functional organization within hippocampus at rest, and (2) compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). This study showed that fHPSFs were arranged along the longitudinal axis that were not comparable to the lamellar structures of aHPSFs. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. Different functional networks also showed preferential connections with different portions of hippocampal subfields.

Dissociable Auditory Cortico-Cerebellar Pathways in the Human Brain Estimated by Intrinsic Functional Connectivity

The cerebellum, a structure historically associated with motor control, has more recently been implicated in several higher-order auditory-cognitive functions. However, the exact functional pathways that mediate cerebellar influences on auditory cortex (AC) remain unclear. Here, we sought to identify auditory cortico-cerebellar pathways based on intrinsic functional connectivity magnetic resonance imaging. In contrast to previous connectivity studies that principally consider the AC as a single functionally homogenous unit, we mapped the cerebellar connectivity across different parts of the AC. Our results reveal that auditory subareas demonstrating different levels of interindividual functional variability are functionally coupled with distinct cerebellar regions. Moreover, auditory and sensorimotor areas show divergent cortico-cerebellar connectivity patterns, although sensorimotor areas proximal to the AC are often functionally grouped with the AC in previous connectivity-based network analyses. Lastly, we found that the AC can be functionally segmented into highly similar subareas based on either cortico-cerebellar or cortico-cortical functional connectivity, suggesting the existence of multiple parallel auditory cortico-cerebellar circuits that involve different subareas of the AC. Overall, the present study revealed multiple auditory cortico-cerebellar pathways and provided a fine-grained map of AC subareas, indicative of the critical role of the cerebellum in auditory processing and multisensory integration.

Correspondence of categorical and feature-based representations of music in the human brain

INTRODUCTION

Humans tend to categorize auditory stimuli into discrete classes, such as animal species, language, musical instrument, and music genre. Of these, music genre is a frequently used dimension of human music preference and is determined based on the categorization of complex auditory stimuli. Neuroimaging studies have reported that the superior temporal gyrus (STG) is involved in response to general music-related features. However, there is considerable uncertainty over how discrete music categories are represented in the brain and which acoustic features are more suited for explaining such representations.

METHODS

We used a total of 540 music clips to examine comprehensive cortical representations and the functional organization of music genre categories. For this purpose, we applied a voxel-wise modeling approach to music-evoked brain activity measured using functional magnetic resonance imaging. In addition, we introduced a novel technique for feature-brain similarity analysis and assessed how discrete music categories are represented based on the cortical response pattern to acoustic features.

RESULTS

Our findings indicated distinct cortical organizations for different music genres in the bilateral STG, and they revealed representational relationships between different music genres. On comparing different acoustic feature models, we found that these representations of music genres could be explained largely by a biologically plausible spectro-temporal modulation-transfer function model.

CONCLUSION

Our findings have elucidated the quantitative representation of music genres in the human cortex, indicating the possibility of modeling this categorization of complex auditory stimuli based on brain activity.

Individual Variability in Functional Organization of the Human and Monkey Auditory Cortex

Accumulating evidence shows that auditory cortex (AC) of humans, and other primates, is involved in more complex cognitive processes than feature segregation only, which are shaped by experience-dependent plasticity and thus likely show substantial individual variability. However, thus far, individual variability of ACs has been considered a methodological impediment rather than a phenomenon of theoretical importance. Here, we examined the variability of ACs using intrinsic functional connectivity patterns in humans and macaques. Our results demonstrate that in humans, interindividual variability is greater near the nonprimary than primary ACs, indicating that variability dramatically increases across the processing hierarchy. ACs are also more variable than comparable visual areas and show higher variability in the left than in the right hemisphere, which may be related to the left lateralization of auditory-related functions such as language. Intriguingly, remarkably similar modality differences and lateralization of variability were also observed in macaques. These connectivity-based findings are consistent with a confirmatory task-based functional magnetic resonance imaging analysis. The quantification of variability in auditory function, and the similar findings in both humans and macaques, will have strong implications for understanding the evolution of advanced auditory functions in humans.

Exploring the neural correlates of self-related names in healthy subjects

OBJECTIVES

This study aimed to clarify the neural correlates and underlying mechanisms of the subject's own name (SON) and the unique name derived from the SON (SDN).

METHODS

A name that was most familiar to the subject (SFN) was added as a self-related reference. We used 4 auditory stimuli-pure tone (1000 Hz), SON, SDN, and SFN-to evaluate the corresponding activated brain areas in 19 healthy subjects by using functional magnetic resonance imaging.

RESULTS

Our results demonstrated that pure tone activated the fewest brain regions. Although SFN was a very strong self-related stimulus, it failed to activate many midline structures. The brain regions activated by SON and SDN were very similar. SFN as a self-related stimulus was less self-related compared with SDN. What's more, the additionally activated fusiform gyrus and parahippocampal gyrus of SDN might revealed its processing path.

CONCLUSIONS

SDN, which has created by us, is a new and self-related stimulus similar to SON. They might provide a useful reference for consciousness assessment with SON and SDN.

Surface-based analysis increases the specificity of cortical activation patterns and connectivity results

Spatial smoothing of functional magnetic resonance imaging (fMRI) data can be performed on volumetric images and on the extracted surface of the brain. Smoothing on the unfolded cortex should theoretically improve the ability to separate signals between brain areas that are near together in the folded cortex but are more distant in the unfolded cortex. However, surface-based method approaches (SBA) are currently not utilized as standard procedure in the preprocessing of neuroimaging data. Recent improvements in the quality of cortical surface modeling and improvements in its usability nevertheless advocate this method. In the current study, we evaluated the benefits of an up-to-date surface-based smoothing in comparison to volume-based smoothing. We focused on the effect of signal contamination between different functional systems using the primary motor and primary somatosensory cortex as an example. We were particularly interested in how this signal contamination influences the results of activity and connectivity analyses for these brain regions. We addressed this question by performing fMRI on 19 subjects during a tactile stimulation paradigm and by using simulated BOLD responses. We demonstrated that volume-based smoothing causes contamination of the primary motor cortex by somatosensory cortical responses, leading to false positive motor activation. These false positive motor activations were not found by using surface-based smoothing for reasonable kernel sizes. Accordingly, volume-based smoothing caused an exaggeration of connectivity estimates between these regions. In conclusion, this study showed that surface-based smoothing decreases signal contamination considerably between neighboring functional brain regions and improves the validity of activity and connectivity results.

Is Human Auditory Cortex Organization Compatible With the Monkey Model? Contrary Evidence From Ultra-High-Field Functional and Structural MRI

It is commonly assumed that the human auditory cortex is organized similarly to that of macaque monkeys, where the primary region, or "core," is elongated parallel to the tonotopic axis (main direction of tonotopic gradients), and subdivided across this axis into up to 3 distinct areas (A1, R, and RT), with separate, mirror-symmetric tonotopic gradients. This assumption, however, has not been tested until now. Here, we used high-resolution ultra-high-field (7 T) magnetic resonance imaging (MRI) to delineate the human core and map tonotopy in 24 individual hemispheres. In each hemisphere, we assessed tonotopic gradients using principled, quantitative analysis methods, and delineated the core using 2 independent (functional and structural) MRI criteria. Our results indicate that, contrary to macaques, the human core is elongated perpendicular rather than parallel to the main tonotopic axis, and that this axis contains no more than 2 mirror-reversed gradients within the core region. Previously suggested homologies between these gradients and areas A1 and R in macaques were not supported. Our findings suggest fundamental differences in auditory cortex organization between humans and macaques.

Cochlear Implantation Outcomes in Children with Agenesis of the Corpus Callosum: A Retrospective Study and A Review of the Literature

OBJECTIVES

The aim of the present study was to analyze the outcomes of cochlear implantation (CI) in patients with agenesis of the corpus callosum (CCA). A literature review and a retrospective analysis of our cochlear implant database were performed.

MATERIALS AND METHODS

To the best of our knowledge, in the English literature, there was only one case reported with CCA who had undergone CI surgery. This case had Donnai-Barrow syndrome. In the Cukurova University School of Medicine Department of Otorhinolaryngology database, 5 of the 1317 patients who underwent CI surgery who had CCA were selected. The patients' demographic characteristics, operative findings, surgical outcomes, and additional disabilities were investigated. The patients' preoperative and postoperative Listening Progress Profile (LiP) and Meaningful Auditory Integration Scale (MAIS) tests were done to analyze the auditory performances.

RESULTS

The participants of the study were 5 (0.38%) individuals (2 male and 3 female patients; ages 5.5, 7.5, 8, 9, and 12 years). Two of the patients had total agenesis, and the other three had partial agenesis of the CCA. In the histories of the patients, one patient had parental consanguinity, and one had febrile convulsion. No patient had an additional disability. None had experienced device failure. No patients were non-users or limited users of cochlear implants. Postoperative LiP and MAIS test scores were improved for all patients nearly as the patients without any deformity. They showed normal auditory performance in the analysis in their postoperative 48 months of follow-up.

CONCLUSION

Patients who had CCA are good candidates for CI surgery.

Investigations on maladaptive plasticity in the sensorimotor cortex of unilateral upper limb CRPS I patients

BACKGROUND

Patients with a complex regional pain syndrome (CRPS) in the upper limb show a sensory and motor impairment of the hand. Decreased intra-cortical-inhibition (ICI) of the motor representation of the affected hand muscle and decreased somatosensory hand representation size were related to maladaptive plasticity.

OBJECTIVE

To achieve new insights about CRPS we examined whether these alterations were present in a single cohort.

METHODS

We used a multi-modal approach comprising behavioral testing, transcranial magnetic stimulation, and high resolution fMRI combined with a new analysis technique for improved neuronal specificity.

RESULTS

We found a decreased pinch-grip performance, two-point discrimination on the fingertips, ICI in the motor cortex, and representation size of the hand in Brodmann Area 3b (BA3b) in the somatosensory cortex. Our analysis further showed that correlations with ICI on the non-affected side were absent on the affected side.

CONCLUSIONS

This study is the first to gather behavioral, neurophysiologic and imaging measurements for one patient cohort and it therefore enables a comprehensive view of collapsed associations of function and representation focused on the hemisphere contralateral to the affected hand.

DC Shifts-fMRI: A Supplement to Event-Related fMRI

Event-related fMRI have been widely used in locating brain regions which respond to specific tasks. However, activities of brain regions which modulate or indirectly participate in the response to a specific task are not event-related. Event-related fMRI can't locate these regulatory regions, detrimental to the integrity of the result that event-related fMRI revealed. Direct-current EEG shifts (DC shifts) have been found linked to the inner brain activity, a fusion DC shifts-fMRI method may have the ability to reveal a more complete response of the brain. In this study, we used DC shifts-fMRI to verify that even when responding to a very simple task, (1) The response of the brain is more complicated than event-related fMRI generally revealed and (2) DC shifts-fMRI have the ability of revealing brain regions whose responses are not in event-related way. We used a classical and simple paradigm which is often used in auditory cortex tonotopic mapping. Data were recorded from 50 subjects (25 male, 25 female) who were presented with randomly presented pure tone sequences with six different frequencies (200, 400, 800, 1,600, 3,200, 6,400 Hz). Our traditional fMRI results are consistent with previous findings that the activations are concentrated on the auditory cortex. Our DC shifts-fMRI results showed that the cingulate-caudate-thalamus network which underpins sustained attention is positively activated while the dorsal attention network and the right middle frontal gyrus which underpin attention orientation are negatively activated. The regional-specific correlations between DC shifts and brain networks indicate the complexity of the response of the brain even to a simple task and that the DC shifts can effectively reflect these non-event-related inner brain activities.

SEGUE: A Speedy rEgion-Growing Algorithm for Unwrapping Estimated Phase

Recent magnetic resonance imaging (MRI) techniques, such as quantitative magnetic susceptibility mapping, employ the signal phase to reveal disease-related changes in tissue composition, including iron or calcium content. The MRI phase is also routinely used in functional and diffusion MRI for distortion correction. However, phase images are wrapped into a range of 2π radians. Phase region expanding labeller for unwrapping discrete estimates (PRELUDE) is the gold standard method for robust, spatial, 3-D, MRI phase unwrapping. Unfortunately, PRELUDE's computation time can reach 15 min for a severely wrapped brain image and nearly 10 h to unwrap a full head-and-neck image on a standard PC. In this paper, we develop a Speedy rEgion-Growing algorithm for Unwrapping Estimated phase (SEGUE) based on similar principles to PRELUDE, implemented with additional methods for acceleration. We compared PRELUDE and SEGUE in numerical phantoms, and using in vivo images of the brain, head and neck, and pelvis acquired in 4-5 healthy volunteers and at 4-6 echo times. To overcome chemical-shift-induced errors within the head and neck, and pelvic images, we also investigated applying both techniques within fat and water masks separately. SEGUE provided almost identical unwrapped phase maps to the gold standard PRELUDE. SEGUE was (1.5 to 70 times) faster than PRELUDE, especially in severely wrapped images at later echoes and in the head and neck, and pelvic images. Applying these techniques within fat and water masks separately removed chemical-shift-induced errors successfully. SEGUE's MATLAB implementation is available for download. SEGUE is a general unwrapping algorithm not specific to MRI, and therefore could be used in images acquired with other modalities.

Intracortical smoothing of small-voxel fMRI data can provide increased detection power without spatial resolution losses compared to conventional large-voxel fMRI data

Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for kernels of various shapes and sizes, including curved 3D kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the kernel configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several kernel configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a kernel of an appropriate shape to achieve the best performance-thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation.

Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex

Frequency preference and spectral tuning are two cardinal features of information processing in the auditory cortex. However, sounds should not only be processed in separate frequency bands because information needs to be integrated to be meaningful. One way to better understand the integration of acoustic information is to examine the functional connectivity across cortical depths, as neurons are already connected differently across laminar layers. Using a tailored receiver array and surface-based cortical depth analysis, we revealed the frequency-preference as well as tuning-width dependent intrinsic functional connectivity (iFC) across cortical depths in the human auditory cortex using functional magnetic resonance imaging (fMRI). We demonstrated feature-dependent iFC in both core and noncore regions at all cortical depths. The selectivity of frequency-preference dependent iFC was higher at deeper depths than at intermediate and superficial depths in the core region. Both the selectivity of frequency-preference and tuning-width dependent iFC were stronger in the core than in the noncore region at deep cortical depths. Taken together, our findings provide evidence for a cortical depth-specific feature-dependent functional connectivity in the human auditory cortex.

Ergodicity and parameter estimates in auditory neural circuits

This paper discusses ergodic properties and circular statistical characteristics in neuronal spike trains. Ergodicity means that the average taken over a long time period and over smaller population should equal the average in less time and larger population. The objectives are to show simple examples of design and validation of a neuronal model, where the ergodicity assumption helps find correspondence between variables and parameters. The methods used are analytical and numerical computations, numerical models of phenomenological spiking neurons and neuronal circuits. Results obtained using these methods are the following. They are: a formula to calculate vector strength of neural spike timing dependent on the spike train parameters, description of parameters of spike train variability and model of output spiking density based on assumption of the computation realized by sound localization neural circuit. Theoretical results are illustrated by references to experimental data. Examples of neurons where spike trains have and do not have the ergodic property are then discussed.

Analysis strategies for high-resolution UHF-fMRI data

Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential.

Functional connectivity corresponding to the tonotopic differentiation of the human auditory cortex

Recent research has demonstrated that resting-state functional connectivity (RS-FC) within the human auditory cortex (HAC) is frequency-selective, but whether RS-FC between the HAC and other brain areas is differentiated by frequency remains unclear. Three types of data were collected in this study, including resting-state functional magnetic resonance imaging (fMRI) data, task-based fMRI data using six pure tone stimuli (200, 400, 800, 1,600, 3,200, and 6,400 Hz), and structural imaging data. We first used task-based fMRI to identify frequency-selective cortical regions in the HAC. Six regions of interest (ROIs) were defined based on the responses of 50 participants to the six pure tone stimuli. Then, these ROIs were used as seeds to determine RS-FC between the HAC and other brain regions. The results showed that there was RS-FC between the HAC and brain regions that included the superior temporal gyrus, dorsolateral prefrontal cortex (DL-PFC), parietal cortex, occipital lobe, and subcortical structures. Importantly, significant differences in FC were observed among most of the brain regions that showed RS-FC with the HAC. Specifically, there was stronger RS-FC between (1) low-frequency (200 and 400 Hz) regions and brain regions including the premotor cortex, somatosensory/-association cortex, and DL-PFC; (2) intermediate-frequency (800 and 1,600 Hz) regions and brain regions including the anterior/posterior superior temporal sulcus, supramarginal gyrus, and inferior frontal cortex; (3) intermediate/low-frequency regions and vision-related regions; (4) high-frequency (3,200 and 6,400 Hz) regions and the anterior cingulate cortex or left DL-PFC. These findings demonstrate that RS-FC between the HAC and other brain areas is frequency selective.

The spatiotemporal pattern of pure tone processing: A single-trial EEG-fMRI study

Although considerable research has been published on pure tone processing, its spatiotemporal pattern is not well understood. Specifically, the link between neural activity in the auditory pathway measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) markers of pure tone processing in the P1, N1, P2, and N4 components is not well established. In this study, we used single-trial EEG-fMRI as a multi-modal fusion approach to integrate concurrently acquired EEG and fMRI data, in order to understand the spatial and temporal aspects of the pure tone processing pathway. Data were recorded from 33 subjects who were presented with stochastically alternating pure tone sequences with two different frequencies: 200 and 6400 Hz. Brain network correlated with trial-to-trial variability of the task-discriminating EEG amplitude was identified. We found that neural responses responding to pure tone perception are spatially along the auditory pathway and temporally divided into three stages: (1) the early stage (P1), wherein activation occurs in the midbrain, which constitutes a part of the low level auditory pathway; (2) the middle stage (N1, P2), wherein correlates were found in areas associated with the posterodorsal auditory pathway, including the primary auditory cortex and the motor cortex; (3) the late stage (N4), wherein correlation was found in the motor cortex. This indicates that trial-by-trial variation in neural activity in the P1, N1, P2, and N4 components reflects the sequential engagement of low- and high-level parts of the auditory pathway for pure tone processing. Our results demonstrate that during simple pure tone listening tasks, regions associated with the auditory pathway transiently correlate with trial-to-trial variability of the EEG amplitude, and they do so on a millisecond timescale with a distinct temporal ordering.

Extensive Tonotopic Mapping across Auditory Cortex Is Recapitulated by Spectrally Directed Attention and Systematically Related to Cortical Myeloarchitecture

Auditory selective attention is vital in natural soundscapes. But it is unclear how attentional focus on the primary dimension of auditory representation—acoustic frequency—might modulate basic auditory functional topography during active listening. In contrast to visual selective attention, which is supported by motor-mediated optimization of input across saccades and pupil dilation, the primate auditory system has fewer means of differentially sampling the world. This makes spectrally-directed endogenous attention a particularly crucial aspect of auditory attention. Using a novel functional paradigm combined with quantitative MRI, we establish in male and female listeners that human frequency-band-selective attention drives activation in both myeloarchitectonically estimated auditory core, and across the majority of tonotopically mapped nonprimary auditory cortex. The attentionally driven best-frequency maps show strong concordance with sensory-driven maps in the same subjects across much of the temporal plane, with poor concordance in areas outside traditional auditory cortex. There is significantly greater activation across most of auditory cortex when best frequency is attended, versus ignored; the same regions do not show this enhancement when attending to the least-preferred frequency band. Finally, the results demonstrate that there is spatial correspondence between the degree of myelination and the strength of the tonotopic signal across a number of regions in auditory cortex. Strong frequency preferences across tonotopically mapped auditory cortex spatially correlate with R₁-estimated myeloarchitecture, indicating shared functional and anatomical organization that may underlie intrinsic auditory regionalization.

SIGNIFICANCE STATEMENT Perception is an active process, especially sensitive to attentional state. Listeners direct auditory attention to track a violin's melody within an ensemble performance, or to follow a voice in a crowded cafe. Although diverse pathologies reduce quality of life by impacting such spectrally directed auditory attention, its neurobiological bases are unclear. We demonstrate that human primary and nonprimary auditory cortical activation is modulated by spectrally directed attention in a manner that recapitulates its tonotopic sensory organization. Further, the graded activation profiles evoked by single-frequency bands are correlated with attentionally driven activation when these bands are presented in complex soundscapes. Finally, we observe a strong concordance in the degree of cortical myelination and the strength of tonotopic activation across several auditory cortical regions.

Laminar fMRI: What can the time domain tell us?

The rapid developments in functional MRI (fMRI) acquisition methods and hardware technologies in recent years, particularly at high field (≥7 T), have enabled unparalleled visualization of functional detail at a laminar or columnar level, bringing fMRI close to the intrinsic resolution of brain function. These advances highlight the potential of high resolution fMRI to be a valuable tool to study the fundamental processing performed in cortical micro-circuits, and their interactions such as feedforward and feedback processes. Notably, because fMRI measures neuronal activity via hemodynamics, the ultimate resolution it affords depends on the spatial specificity of hemodynamics to neuronal activity at a detailed spatial scale, and by the evolution of this specificity over time. Several laminar (≤1 mm spatial resolution) fMRI studies have examined spatial characteristics of the measured hemodynamic signals across cortical depth, in light of understanding or improving the spatial specificity of laminar fMRI. Few studies have examined temporal features of the hemodynamic response across cortical depth. Temporal features of the hemodynamic response offer an additional means to improve the specificity of fMRI, and could help target neuronal processes and neurovascular coupling relationships across laminae, for example by differences in the onset times of the response across cortical depth. In this review, we discuss factors that affect the timing of neuronal and hemodynamic responses across laminae, touching on the neuronal laminar organization, and focusing on the laminar vascular organization. We provide an overview of hemodynamics across the cortical vascular tree based on optical imaging studies, and review temporal aspects of hemodynamics that have been examined across cortical depth in high spatiotemporal resolution fMRI studies. Last, we discuss the limits and potential of high spatiotemporal resolution fMRI to study laminar neurovascular coupling and neuronal processes.

High resolution data analysis strategies for mesoscale human functional MRI at 7 and 9.4T

The advent of ultra-high field functional magnetic resonance imaging (fMRI) has greatly facilitated submillimeter resolution acquisitions (voxel volume below (1 mm³)), allowing the investigation of cortical columns and cortical depth dependent (i.e. laminar) structures in the human brain. Advanced data analysis techniques are essential to exploit the information in high resolution functional measures. In this article, we use recent, exemplary 9.4 T human functional and anatomical data to review the advantages and disadvantages of (1) pooling high resolution data across regions of interest for cortical depth profile analysis, (2) pooling across cortical depths for mapping patches of cortex while discarding depth-dependent (i.e. columnar) effects, and (3) isotropic sampling without pooling to assess individual voxel’s responses. A set of cortical depth meshes may be a solution to sampling information tangentially while keeping correspondence across depths. For quantitative analysis of the spatial organization in fine-grained structures, a cortical grid approach is advantageous. We further extend this general framework by combining it with a previously introduced cortical layer volume-preserving (equi-volume) approach. This framework can readily accommodate the research questions which allow for spatial smoothing within or across layers. We demonstrate and discuss that equi-volume sampling yields a slight advantage over equidistant sampling given the current limitations of fMRI voxel size, participant motion, coregistration and segmentation. Our 9.4 T human anatomical and functional data indicate the advantage over lower fields including 7 T and demonstrate the practical applicability of T₂^* and T₂-weighted fMRI acquisitions.

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High resolution regular cortical grids are advantageous for local applications.

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Equi-volume sampling is slightly advantageous over equidistant sampling in-vivo.

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Isotropic submillimeter cortical sampling without spatial pooling requires high SNR.

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9.4 T human T2 and T2* BOLD fMRI are practically feasible and provide high SNR.

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9.4 T T2*-weighted 0.35 mm iso. res. anatomical images for laminar contrast in vivo.

The impact of ultra-high field MRI on cognitive and computational neuroimaging

The ability to measure functional brain responses non-invasively with ultra high field MRI (7 T and above) represents a unique opportunity in advancing our understanding of the human brain. Compared to lower fields (3 T and below), ultra high field MRI has an increased sensitivity, which can be used to acquire functional images with greater spatial resolution, and greater specificity of the blood oxygen level dependent (BOLD) signal to the underlying neuronal responses. Together, increased resolution and specificity enable investigating brain functions at a submillimeter scale, which so far could only be done with invasive techniques. At this mesoscopic spatial scale, perception, cognition and behavior can be probed at the level of fundamental units of neural computations, such as cortical columns, cortical layers, and subcortical nuclei. This represents a unique and distinctive advantage that differentiates ultra high from lower field imaging and that can foster a tighter link between fMRI and computational modeling of neural networks. So far, functional brain mapping at submillimeter scale has focused on the processing of sensory information and on well-known systems for which extensive information is available from invasive recordings in animals. It remains an open challenge to extend this methodology to uniquely human functions and, more generally, to systems for which animal models may be problematic. To succeed, the possibility to acquire high-resolution functional data with large spatial coverage, the availability of computational models of neural processing as well as accurate biophysical modeling of neurovascular coupling at mesoscopic scale all appear necessary.