Linguistic modulation of the neural encoding of phonemes.

Speech comprehension entails the neural mapping of the acoustic speech signal onto learned linguistic units. This acousto-linguistic transformation is bi-directional, whereby higher-level linguistic processes (e.g. semantics) modulate the acoustic analysis of individual linguistic units. Here, we investigated the cortical topography and linguistic modulation of the most fundamental linguistic unit, the phoneme. We presented natural speech and "phoneme quilts" (pseudo-randomly shuffled phonemes) in either a familiar (English) or unfamiliar (Korean) language to native English speakers while recording functional magnetic resonance imaging. This allowed us to dissociate the contribution of acoustic vs. linguistic processes toward phoneme analysis. We show that (i) the acoustic analysis of phonemes is modulated by linguistic analysis and (ii) that for this modulation, both of acoustic and phonetic information need to be incorporated. These results suggest that the linguistic modulation of cortical sensitivity to phoneme classes minimizes prediction error during natural speech perception, thereby aiding speech comprehension in challenging listening situations.

Selective attention sharpens population receptive fields in human auditory cortex.

Selective attention enables the preferential processing of relevant stimulus aspects. Invasive animal studies have shown that attending a sound feature rapidly modifies neuronal tuning throughout the auditory cortex. Human neuroimaging studies have reported enhanced auditory cortical responses with selective attention. To date, it remains unclear how the results obtained with functional magnetic resonance imaging (fMRI) in humans relate to the electrophysiological findings in animal models. Here we aim to narrow the gap between animal and human research by combining a selective attention task similar in design to those used in animal electrophysiology with high spatial resolution ultra-high field fMRI at 7 Tesla. Specifically, human participants perform a detection task, whereas the probability of target occurrence varies with sound frequency. Contrary to previous fMRI studies, we show that selective attention resulted in population receptive field sharpening, and consequently reduced responses, at the attended sound frequencies. The difference between our results to those of previous fMRI studies supports the notion that the influence of selective attention on auditory cortex is diverse and may depend on context, stimulus, and task.

Voxelwise encoding models of body stimuli reveal a representational gradient from low-level visual features to postural features in occipitotemporal cortex.

Previous research on body representation in the brain has focused on category-specific representation, using fMRI to investigate the response pattern to body stimuli in occipitotemporal cortex. But the central question of the specific computations involved in body selective regions has not been addressed so far. This study used ultra-high field fMRI and banded ridge regression to investigate the computational mechanisms of coding body images, by comparing the performance of three encoding models in predicting brain activity in occipitotemporal cortex and specifically in the extrastriate body area (EBA). Our results indicate that bodies are encoded in occipitotemporal cortex and in the EBA according to a combination of low-level visual features and postural features.

Magnetic resonance imaging at 9.4 T: the Maastricht journey.

The 9.4 T scanner in Maastricht is a whole-body magnet with head gradients and parallel RF transmit capability. At the time of the design, it was conceptualized to be one of the best fMRI scanners in the world, but it has also been used for anatomical and diffusion imaging. 9.4 T offers increases in sensitivity and contrast, but the technical ultra-high field (UHF) challenges, such as field inhomogeneities and constraints set by RF power deposition, are exacerbated compared to 7 T. This article reviews some of the 9.4 T work done in Maastricht. Functional imaging experiments included blood oxygenation level-dependent (BOLD) and blood-volume weighted (VASO) fMRI using different readouts. BOLD benefits from shorter T2* at 9.4 T while VASO from longer T1. We show examples of both ex vivo and in vivo anatomical imaging. For many applications, pTx and optimized coils are essential to harness the full potential of 9.4 T. Our experience shows that, while considerable effort was required compared to our 7 T scanner, we could obtain high-quality anatomical and functional data, which illustrates the potential of MR acquisitions at even higher field strengths. The practical challenges of working with a relatively unique system are also discussed.

Columnar clusters in the human motion complex reflect consciously perceived motion axis.

The specific contents of human consciousness rely on the activity of specialized neurons in cerebral cortex. We hypothesized that the conscious experience of a specific visual motion axis is reflected in response amplitudes of direction-selective clusters in the human motion complex. Using submillimeter fMRI at ultrahigh field (7 T) we identified fine-grained clusters that were tuned to either horizontal or vertical motion presented in an unambiguous motion display. We then recorded their responses while human observers reported the perceived axis of motion for an ambiguous apparent motion display. Although retinal stimulation remained constant, subjects reported recurring changes between horizontal and vertical motion percepts every 7 to 13 s. We found that these perceptual states were dissociatively reflected in the response amplitudes of the identified horizontal and vertical clusters. We also found that responses to unambiguous motion were organized in a columnar fashion such that motion preferences were stable in the direction of cortical depth and changed when moving along the cortical surface. We suggest that activity in these specialized clusters is involved in tracking the distinct conscious experience of a particular motion axis.

Cross-validation and permutations in MVPA: Validity of permutation strategies and power of cross-validation schemes.

Multi-Voxel Pattern Analysis (MVPA) is a well established tool to disclose weak, distributed effects in brain activity patterns. The generalization ability is assessed by testing the learning model on new, unseen data. However, when limited data is available, the decoding success is estimated using cross-validation. There is general consensus on assessing statistical significance of cross-validated accuracy with non-parametric permutation tests. In this work we focus on the false positive control of different permutation strategies and on the statistical power of different cross-validation schemes. With simulations, we show that estimating the entire cross-validation error on each permuted dataset is the only statistically valid permutation strategy. Furthermore, using both simulations and real data from the HCP WU-Minn 3T fMRI dataset, we show that, among the different cross-validation schemes, a repeated split-half cross-validation is the most powerful, despite achieving slightly lower classification accuracy, when compared to other schemes. Our findings provide additional insights into the optimization of the experimental design for MVPA, highlighting the benefits of having many short runs.

Methods for computing the maximum performance of computational models of fMRI responses.

Computational neuroimaging methods aim to predict brain responses (measured e.g. with functional magnetic resonance imaging [fMRI]) on the basis of stimulus features obtained through computational models. The accuracy of such prediction is used as an indicator of how well the model describes the computations underlying the brain function that is being considered. However, the prediction accuracy is bounded by the proportion of the variance of the brain response which is related to the measurement noise and not to the stimuli (or cognitive functions). This bound to the performance of a computational model has been referred to as the noise ceiling. In previous fMRI applications two methods have been proposed to estimate the noise ceiling based on either a split-half procedure or Monte Carlo simulations. These methods make different assumptions over the nature of the effects underlying the data, and, importantly, their relation has not been clarified yet. Here, we derive an analytical form for the noise ceiling that does not require computationally expensive simulations or a splitting procedure that reduce the amount of data. The validity of this analytical definition is proved in simulations, we show that the analytical solution results in the same estimate of the noise ceiling as the Monte Carlo method. Considering different simulated noise structure, we evaluate different estimators of the variance of the responses and their impact on the estimation of the noise ceiling. We furthermore evaluate the interplay between regularization (often used to estimate model fits to the data when the number of computational features in the model is large) and model complexity on the performance with respect to the noise ceiling. Our results indicate that when considering the variance of the responses across runs, computing the noise ceiling analytically results in similar estimates as the split half estimator and approaches the true noise ceiling under a variety of simulated noise scenarios. Finally, the methods are tested on real fMRI data acquired at 7 Tesla.

Homology and Specificity of Natural Sound-Encoding in Human and Monkey Auditory Cortex.

Understanding homologies and differences in auditory cortical processing in human and nonhuman primates is an essential step in elucidating the neurobiology of speech and language. Using fMRI responses to natural sounds, we investigated the representation of multiple acoustic features in auditory cortex of awake macaques and humans. Comparative analyses revealed homologous large-scale topographies not only for frequency but also for temporal and spectral modulations. In both species, posterior regions preferably encoded relatively fast temporal and coarse spectral information, whereas anterior regions encoded slow temporal and fine spectral modulations. Conversely, we observed a striking interspecies difference in cortical sensitivity to temporal modulations: While decoding from macaque auditory cortex was most accurate at fast rates (> 30 Hz), humans had highest sensitivity to ~3 Hz, a relevant rate for speech analysis. These findings suggest that characteristic tuning of human auditory cortex to slow temporal modulations is unique and may have emerged as a critical step in the evolution of speech and language.

Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns.

Ethological views of brain functioning suggest that sound representations and computations in the auditory neural system are optimized finely to process and discriminate behaviorally relevant acoustic features and sounds (e.g., spectrotemporal modulations in the songs of zebra finches). Here, we show that modeling of neural sound representations in terms of frequency-specific spectrotemporal modulations enables accurate and specific reconstruction of real-life sounds from high-resolution functional magnetic resonance imaging (fMRI) response patterns in the human auditory cortex. Region-based analyses indicated that response patterns in separate portions of the auditory cortex are informative of distinctive sets of spectrotemporal modulations. Most relevantly, results revealed that in early auditory regions, and progressively more in surrounding regions, temporal modulations in a range relevant for speech analysis (∼2-4 Hz) were reconstructed more faithfully than other temporal modulations. In early auditory regions, this effect was frequency-dependent and only present for lower frequencies (<∼2 kHz), whereas for higher frequencies, reconstruction accuracy was higher for faster temporal modulations. Further analyses suggested that auditory cortical processing optimized for the fine-grained discrimination of speech and vocal sounds underlies this enhanced reconstruction accuracy. In sum, the present study introduces an approach to embed models of neural sound representations in the analysis of fMRI response patterns. Furthermore, it reveals that, in the human brain, even general purpose and fundamental neural processing mechanisms are shaped by the physical features of real-world stimuli that are most relevant for behavior (i.e., speech, voice).

Mapping Frequency-Specific Tone Predictions in the Human Auditory Cortex at High Spatial Resolution.

Auditory inputs reaching our ears are often incomplete, but our brains nevertheless transform them into rich and complete perceptual phenomena such as meaningful conversations or pleasurable music. It has been hypothesized that our brains extract regularities in inputs, which enables us to predict the upcoming stimuli, leading to efficient sensory processing. However, it is unclear whether tone predictions are encoded with similar specificity as perceived signals. Here, we used high-field fMRI to investigate whether human auditory regions encode one of the most defining characteristics of auditory perception: the frequency of predicted tones. Two pairs of tone sequences were presented in ascending or descending directions, with the last tone omitted in half of the trials. Every pair of incomplete sequences contained identical sounds, but was associated with different expectations about the last tone (a high- or low-frequency target). This allowed us to disambiguate predictive signaling from sensory-driven processing. We recorded fMRI responses from eight female participants during passive listening to complete and incomplete sequences. Inspection of specificity and spatial patterns of responses revealed that target frequencies were encoded similarly during their presentations, as well as during omissions, suggesting frequency-specific encoding of predicted tones in the auditory cortex (AC). Importantly, frequency specificity of predictive signaling was observed already at the earliest levels of auditory cortical hierarchy: in the primary AC. Our findings provide evidence for content-specific predictive processing starting at the earliest cortical levels.SIGNIFICANCE STATEMENT Given the abundance of sensory information around us in any given moment, it has been proposed that our brain uses contextual information to prioritize and form predictions about incoming signals. However, there remains a surprising lack of understanding of the specificity and content of such prediction signaling; for example, whether a predicted tone is encoded with similar specificity as a perceived tone. Here, we show that early auditory regions encode the frequency of a tone that is predicted yet omitted. Our findings contribute to the understanding of how expectations shape sound processing in the human auditory cortex and provide further insights into how contextual information influences computations in neuronal circuits.

Evaluating the Columnar Stability of Acoustic Processing in the Human Auditory Cortex.

Using ultra-high field fMRI, we explored the cortical depth-dependent stability of acoustic feature preference in human auditory cortex. We collected responses from human auditory cortex (subjects from either sex) to a large number of natural sounds at submillimeter spatial resolution, and observed that these responses were well explained by a model that assumes neuronal population tuning to frequency-specific spectrotemporal modulations. We observed a relatively stable (columnar) tuning to frequency and temporal modulations. However, spectral modulation tuning was variable throughout the cortical depth. This difference in columnar stability between feature maps could not be explained by a difference in map smoothness, as the preference along the cortical sheet varied in a similar manner for the different feature maps. Furthermore, tuning to all three features was more columnar in primary than nonprimary auditory cortex. The observed overall lack of overlapping columnar regions across acoustic feature maps suggests, especially for primary auditory cortex, a coding strategy in which across cortical depths tuning to some features is kept stable, whereas tuning to other features systematically varies.SIGNIFICANCE STATEMENT In the human auditory cortex, sound aspects are processed in large-scale maps. Invasive animal studies show that an additional processing organization may be implemented orthogonal to the cortical sheet (i.e., in the columnar direction), but it is unknown whether observed organizational principles apply to the human auditory cortex. Combining ultra-high field fMRI with natural sounds, we explore the columnar organization of various sound aspects. Our results suggest that the human auditory cortex contains a modular coding strategy, where, for each module, several sound aspects act as an anchor along which computations are performed while the processing of another sound aspect undergoes a transformation. This strategy may serve to optimally represent the content of our complex acoustic natural environment.

Approaching the Ground Truth: Revealing the Functional Organization of Human Multisensory STC Using Ultra-High Field fMRI.

Integrating inputs across sensory systems is a property of the brain that is vitally important in everyday life. More than two decades of fMRI research have revealed crucial insights on multisensory processing, yet the multisensory operations at the neuronal level in humans have remained largely unknown. Understanding the fine-scale spatial organization of multisensory brain regions is fundamental to shed light on their neuronal operations. Monkey electrophysiology revealed that the bimodal superior temporal cortex (bSTC) is topographically organized according to the modality preference (visual, auditory, and bimodal) of its neurons. In line with invasive studies, a previous 3 Tesla fMRI study suggests that the human bSTC is also topographically organized according to modality preference (visual, auditory, and bimodal) when analyzed at 1.6 × 1.6 × 1.6 mm3 voxel resolution. However, it is still unclear whether this resolution is able to unveil an accurate spatial organization of the human bSTC. This issue was addressed in the present study by investigating the spatial organization of functional responses of the bSTC in 10 participants (from both sexes) at 1.5 × 1.5 × 1.5 mm3 and 1.1 × 1.1 × 1.1 mm3 using ultra-high field fMRI (at 7 Tesla). Relative to 1.5 × 1.5 × 1.5 mm3, the bSTC at 1.1 × 1.1 × 1.1 mm3 resolution was characterized by a larger selectivity for visual and auditory modalities, stronger integrative responses in bimodal voxels, and it was organized in more distinct functional clusters indicating a more precise separation of underlying neuronal clusters. Our findings indicate that increasing the spatial resolution may be necessary and sufficient to achieve a more accurate functional topography of human multisensory integration.SIGNIFICANCE STATEMENT The bimodal superior temporal cortex (bSTC) is a brain region that plays a crucial role in the integration of visual and auditory inputs. The aim of the present study was to investigate the fine-scale spatial organization of the bSTC by using ultra-high magnetic field fMRI at 7 Tesla. Mapping the functional topography of bSTC at a resolution of 1.1 × 1.1 × 1.1 mm3 revealed more accurate representations than at lower resolutions. This result indicates that standard-resolution fMRI may lead to wrong conclusions about the functional organization of the bSTC, whereas high spatial resolution is essential to more accurately approach neuronal operations of human multisensory integration.

Cortical fibers orientation mapping using in-vivo whole brain 7 T diffusion MRI.

Diffusion MRI of the cortical gray matter is challenging because the micro-environment probed by water molecules is much more complex than within the white matter. High spatial and angular resolutions are therefore necessary to uncover anisotropic diffusion patterns and laminar structures, which provide complementary (e.g. to anatomical and functional MRI) microstructural information about the cortex architectonic. Several ex-vivo and in-vivo MRI studies have recently addressed this question, however predominantly with an emphasis on specific cortical areas. There is currently no whole brain in-vivo data leveraging multi-shell diffusion MRI acquisition at high spatial resolution, and depth dependent analysis, to characterize the complex organization of cortical fibers. Here, we present unique in-vivo human 7T diffusion MRI data, and a dedicated cortical depth dependent analysis pipeline. We leverage the high spatial (1.05 mm isotropic) and angular (198 diffusion gradient directions) resolution of this whole brain dataset to improve cortical fiber orientations mapping, and study neurites (axons and/or dendrites) trajectories across cortical depths. Tangential fibers in superficial cortical depths and crossing fiber configurations in deep cortical depths are identified. Fibers gradually inserting into the gyral walls are visualized, which contributes to mitigating the gyral bias effect. Quantitative radiality maps and histograms in individual subjects and cortex-based aligned datasets further support our results.

Cortical processing of pitch: Model-based encoding and decoding of auditory fMRI responses to real-life sounds.

Pitch is a perceptual attribute related to the fundamental frequency (or periodicity) of a sound. So far, the cortical processing of pitch has been investigated mostly using synthetic sounds. However, the complex harmonic structure of natural sounds may require different mechanisms for the extraction and analysis of pitch. This study investigated the neural representation of pitch in human auditory cortex using model-based encoding and decoding analyses of high field (7 T) functional magnetic resonance imaging (fMRI) data collected while participants listened to a wide range of real-life sounds. Specifically, we modeled the fMRI responses as a function of the sounds' perceived pitch height and salience (related to the fundamental frequency and the harmonic structure respectively), which we estimated with a computational algorithm of pitch extraction (de Cheveigné and Kawahara, 2002). First, using single-voxel fMRI encoding, we identified a pitch-coding region in the antero-lateral Heschl's gyrus (HG) and adjacent superior temporal gyrus (STG). In these regions, the pitch representation model combining height and salience predicted the fMRI responses comparatively better than other models of acoustic processing and, in the right hemisphere, better than pitch representations based on height/salience alone. Second, we assessed with model-based decoding that multi-voxel response patterns of the identified regions are more informative of perceived pitch than the remainder of the auditory cortex. Further multivariate analyses showed that complementing a multi-resolution spectro-temporal sound representation with pitch produces a small but significant improvement to the decoding of complex sounds from fMRI response patterns. In sum, this work extends model-based fMRI encoding and decoding methods - previously employed to examine the representation and processing of acoustic sound features in the human auditory system - to the representation and processing of a relevant perceptual attribute such as pitch. Taken together, the results of our model-based encoding and decoding analyses indicated that the pitch of complex real life sounds is extracted and processed in lateral HG/STG regions, at locations consistent with those indicated in several previous fMRI studies using synthetic sounds. Within these regions, pitch-related sound representations reflect the modulatory combination of height and the salience of the pitch percept.

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 (1mm³)), 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 9.4T 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 9.4T human anatomical and functional data indicate the advantage over lower fields including 7T and demonstrate the practical applicability of T2* and T2-weighted fMRI acquisitions.

Encoding of natural timbre dimensions in human auditory cortex.

Timbre, or sound quality, is a crucial but poorly understood dimension of auditory perception that is important in describing speech, music, and environmental sounds. The present study investigates the cortical representation of different timbral dimensions. Encoding models have typically incorporated the physical characteristics of sounds as features when attempting to understand their neural representation with functional MRI. Here we test an encoding model that is based on five subjectively derived dimensions of timbre to predict cortical responses to natural orchestral sounds. Results show that this timbre model can outperform other models based on spectral characteristics, and can perform as well as a complex joint spectrotemporal modulation model. In cortical regions at the medial border of Heschl's gyrus, bilaterally, and regions at its posterior adjacency in the right hemisphere, the timbre model outperforms even the complex joint spectrotemporal modulation model. These findings suggest that the responses of cortical neuronal populations in auditory cortex may reflect the encoding of perceptual timbre dimensions.

Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field.

Following rapid technological advances, ultra-high field functional MRI (fMRI) enables exploring correlates of neuronal population activity at an increasing spatial resolution. However, as the fMRI blood-oxygenation-level-dependent (BOLD) contrast is a vascular signal, the spatial specificity of fMRI data is ultimately determined by the characteristics of the underlying vasculature. At 7T, fMRI measurement parameters determine the relative contribution of the macro- and microvasculature to the acquired signal. Here we investigate how these parameters affect relevant high-end fMRI analyses such as encoding, decoding, and submillimeter mapping of voxel preferences in the human auditory cortex. Specifically, we compare a T2* weighted fMRI dataset, obtained with 2D gradient echo (GE) EPI, to a predominantly T2 weighted dataset obtained with 3D GRASE. We first investigated the decoding accuracy based on two encoding models that represented different hypotheses about auditory cortical processing. This encoding/decoding analysis profited from the large spatial coverage and sensitivity of the T2* weighted acquisitions, as evidenced by a significantly higher prediction accuracy in the GE-EPI dataset compared to the 3D GRASE dataset for both encoding models. The main disadvantage of the T2* weighted GE-EPI dataset for encoding/decoding analyses was that the prediction accuracy exhibited cortical depth dependent vascular biases. However, we propose that the comparison of prediction accuracy across the different encoding models may be used as a post processing technique to salvage the spatial interpretability of the GE-EPI cortical depth-dependent prediction accuracy. Second, we explored the mapping of voxel preferences. Large-scale maps of frequency preference (i.e., tonotopy) were similar across datasets, yet the GE-EPI dataset was preferable due to its larger spatial coverage and sensitivity. However, submillimeter tonotopy maps revealed biases in assigned frequency preference and selectivity for the GE-EPI dataset, but not for the 3D GRASE dataset. Thus, a T2 weighted acquisition is recommended if high specificity in tonotopic maps is required. In conclusion, different fMRI acquisitions were better suited for different analyses. It is therefore critical that any sequence parameter optimization considers the eventual intended fMRI analyses and the nature of the neuroscience questions being asked.

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.

Frequency preference and attention effects across cortical depths in the human primary auditory cortex.

Columnar arrangements of neurons with similar preference have been suggested as the fundamental processing units of the cerebral cortex. Within these columnar arrangements, feed-forward information enters at middle cortical layers whereas feedback information arrives at superficial and deep layers. This interplay of feed-forward and feedback processing is at the core of perception and behavior. Here we provide in vivo evidence consistent with a columnar organization of the processing of sound frequency in the human auditory cortex. We measure submillimeter functional responses to sound frequency sweeps at high magnetic fields (7 tesla) and show that frequency preference is stable through cortical depth in primary auditory cortex. Furthermore, we demonstrate that-in this highly columnar cortex-task demands sharpen the frequency tuning in superficial cortical layers more than in middle or deep layers. These findings are pivotal to understanding mechanisms of neural information processing and flow during the active perception of sounds.

Human habenula segmentation using myelin content.

The habenula consists of a pair of small epithalamic nuclei located adjacent to the dorsomedial thalamus. Despite increasing interest in imaging the habenula due to its critical role in mediating subcortical reward circuitry, in vivo neuroimaging research targeting the human habenula has been limited by its small size and low anatomical contrast. In this work, we have developed an objective semi-automated habenula segmentation scheme consisting of histogram-based thresholding, region growing, geometric constraints, and partial volume estimation steps. This segmentation scheme was designed around in vivo 3 T myelin-sensitive images, generated by taking the ratio of high-resolution T1w over T2w images. Due to the high myelin content of the habenula, the contrast-to-noise ratio with the thalamus in the in vivo 3T myelin-sensitive images was significantly higher than the T1w or T2w images alone. In addition, in vivo 7 T myelin-sensitive images (T1w over T2*w ratio images) and ex vivo proton density-weighted images, along with histological evidence from the literature, strongly corroborated the in vivo 3 T habenula myelin contrast used in the proposed segmentation scheme. The proposed segmentation scheme represents a step toward a scalable approach for objective segmentation of the habenula suitable for both morphological evaluation and habenula seed region selection in functional and diffusion MRI applications.