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 feedforward 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.
Recent functional MRI (fMRI) studies have highlighted differences in responses to natural sounds along the rostral-caudal axis of the human superior temporal gyrus. However, due to the indirect ...nature of the fMRI signal, it has been challenging to relate these fMRI observations to actual neuronal response properties. To bridge this gap, we present a forward model of the fMRI responses to natural sounds combining a neuronal model of the auditory cortex with physiological modeling of the hemodynamic BOLD response. Neuronal responses are modeled with a dynamic recurrent firing rate model, reflecting the tonotopic, hierarchical processing in the auditory cortex along with the spectro-temporal tradeoff in the rostral-caudal axis of its belt areas. To link modeled neuronal response properties with human fMRI data in the auditory belt regions, we generated a space of neuronal models, which differed parametrically in spectral and temporal specificity of neuronal responses. Then, we obtained predictions of fMRI responses through a biophysical model of the hemodynamic BOLD response (P-DCM). Using Bayesian model comparison, our results showed that the hemodynamic BOLD responses of the caudal belt regions in the human auditory cortex were best explained by modeling faster temporal dynamics and broader spectral tuning of neuronal populations, while rostral belt regions were best explained through fine spectral tuning combined with slower temporal dynamics. These results support the hypotheses of complementary neural information processing along the rostral-caudal axis of the human superior temporal gyrus.
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).
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.
•Increasing field strength provides advantages in studying brain function in vivo.•High fields allow imaging functional subdivisions of sub-cortical regions.•At high fields functional imaging can be performed at the mesoscopic scale.•At the mesoscale brain inspired computational models can be tested and developed.•Laminar neourvascular coupling models need updating to extend applications.
Transcranial magnetic stimulation (TMS) is a tool for inducing transient disruptions of neural activity noninvasively in conscious human volunteers. In recent years, the investigative domain of TMS ...has expanded and now encompasses causal structure–function relationships across the whole gamut of cognitive functions and associated cortical brain regions. Consequently, the importance of how to determine the target stimulation site has increased and a number of alternative methods have emerged. Comparison across studies is precluded because different studies necessarily use different tasks, sites, TMS conditions, and have different goals. Here, therefore, we systematically compare four commonly used TMS coil positioning approaches by using them to induce behavioral change in a single cognitive study. Specifically, we investigated the behavioral impact of right parietal TMS during a number comparison task, while basing TMS localization either on (i) individual fMRI-guided TMS neuronavigation, (ii) individual MRI-guided TMS neuronavigation, (iii) group functional Talairach coordinates, or (iv) 10–20 EEG position P4. We quantified the exact behavioral effects induced by TMS using each approach, calculated the standardized experimental effect sizes, and conducted a statistical power analysis in order to calculate the optimal sample size required to reveal statistical significance. Our findings revealed a systematic difference between the four approaches, with the individual fMRI-guided TMS neuronavigation yielding the strongest and the P4 stimulation approach yielding the smallest behavioral effect size. Accordingly, power analyses revealed that although in the fMRI-guided neuronavigation approach five participants were sufficient to reveal a significant behavioral effect, the number of necessary participants increased to
= 9 when employing MRI-guided neuronavigation, to
= 13 in case of TMS based on group Talairach coordinates, and to
= 47 when applying TMS over P4. We discuss these graded effect size differences in light of the revealed interindividual variances in the actual target stimulation site within and between approaches.
•We implement homeostatic plasticity (HSP) in an auditory cortex computational model•After HSP, model behavior shows neural signatures of tinnitus•Increased neural noise and oscillations match human ...neuroimaging findings•The proposed model can serve to design future human tinnitus studies
Tinnitus is a clinical condition where a sound is perceived without an external sound source. Homeostatic plasticity (HSP), serving to increase neural activity as compensation for the reduced input to the auditory pathway after hearing loss, has been proposed as a mechanism underlying tinnitus. In support, animal models of tinnitus show evidence of increased neural activity after hearing loss, including increased spontaneous and sound-driven firing rate, as well as increased neural noise throughout the auditory processing pathway. Bridging these findings to human tinnitus, however, has proven to be challenging. Here we implement hearing loss-induced HSP in a Wilson-Cowan Cortical Model of the auditory cortex to predict how homeostatic principles operating at the microscale translate to the meso- to macroscale accessible through human neuroimaging. We observed HSP-induced response changes in the model that were previously proposed as neural signatures of tinnitus, but that have also been reported as correlates of hearing loss and hyperacusis. As expected, HSP increased spontaneous and sound-driven responsiveness in hearing-loss affected frequency channels of the model. We furthermore observed evidence of increased neural noise and the appearance of spatiotemporal modulations in neural activity, which we discuss in light of recent human neuroimaging findings. Our computational model makes quantitative predictions that require experimental validation, and may thereby serve as the basis of future human studies of hearing loss, tinnitus, and hyperacusis.
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.
•Pitch processing is analyzed with a model-based fMRI encoding/decoding approach•The perceived pitch is modeled as a combination of height and salience•Lateral HG and STG responses reflect pitch height and salience•Pitch information improves the decoding of the fMRI responses to natural sounds
The layers of the neocortex each have a unique anatomical connectivity and functional role. Their exploration in the human brain, however, has been severely restricted by the limited spatial ...resolution of non-invasive measurement techniques. Here, we exploit the sensitivity and specificity of ultra-high field fMRI at 7 Tesla to investigate responses to natural sounds at deep, middle, and superficial cortical depths of the human auditory cortex. Specifically, we compare the performance of computational models that represent different hypotheses on sound processing inside and outside the primary auditory cortex (PAC). We observe that while BOLD responses in deep and middle PAC layers are equally well represented by a simple frequency model and a more complex spectrotemporal modulation model, responses in superficial PAC are better represented by the more complex model. This indicates an increase in processing complexity in superficial PAC, which remains present throughout cortical depths in the non-primary auditory cortex. These results suggest that a relevant transformation in sound processing takes place between the thalamo-recipient middle PAC layers and superficial PAC. This transformation may be a first computational step towards sound abstraction and perception, serving to form an increasingly more complex representation of the physical input.
To date it remains largely unknown how fundamental aspects of natural sounds, such as their spectral content and location in space, are processed in human subcortical structures. Here we exploited ...the high sensitivity and specificity of high field fMRI (7 Tesla) to examine the human inferior colliculus (IC) and medial geniculate body (MGB). Subcortical responses to natural sounds were well explained by an encoding model of sound processing that represented frequency and location jointly. Frequency tuning was organized in one tonotopic gradient in the IC, whereas two tonotopic maps characterized the MGB reflecting two MGB subdivisions. In contrast, no topographic pattern of preferred location was detected, beyond an overall preference for peripheral (as opposed to central) and contralateral locations. Our findings suggest the functional organization of frequency and location processing in human subcortical auditory structures, and pave the way for studying the subcortical to cortical interaction required to create coherent auditory percepts.
Neural processing of sounds in the dorsal and ventral streams of the (human) auditory cortex is optimized for analyzing fine-grained temporal and spectral information, respectively. Here we use a ...Wilson and Cowan firing-rate modeling framework to simulate spectro-temporal processing of sounds in these auditory streams and to investigate the link between neural population activity and behavioral results of psychoacoustic experiments. The proposed model consisted of two
(A1 and R, representing primary areas) and two
(
and
, representing rostral and caudal processing respectively) areas, differing in terms of their spectral and temporal response properties. First, we simulated the responses to amplitude modulated (AM) noise and tones. In agreement with electrophysiological results, we observed an area-dependent transition from a temporal (synchronization) to a rate code when moving from low to high modulation rates. Simulated neural responses in a task of amplitude modulation detection suggested that thresholds derived from population responses in
areas closely resembled those of psychoacoustic experiments in human listeners. For tones, simulated modulation threshold functions were found to be dependent on the carrier frequency. Second, we simulated the responses to complex tones with missing fundamental stimuli and found that synchronization of responses in the
area accurately encoded pitch, with the strength of synchronization depending on number and order of harmonic components. Finally, using speech stimuli, we showed that the spectral and temporal structure of the speech was reflected in parallel by the modeled areas. The analyses highlighted that the
stream coded with high spectral precision the aspects of the speech signal characterized by slow temporal changes (e.g., prosody), while the
stream encoded primarily the faster changes (e.g., phonemes, consonants, temporal pitch). Interestingly, the pitch of a speaker was encoded both spatially (i.e., tonotopically) in
area and temporally in
area. Overall, performed simulations showed that the model is valuable for generating hypotheses on how the different cortical areas/streams may contribute toward behaviorally relevant aspects of auditory processing. The model can be used in combination with physiological models of neurovascular coupling to generate predictions for human functional MRI experiments.
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