Summary Tinnitus is a common disorder characterized by ringing in the ear in the absence of sound. Converging evidence suggests that tinnitus pathophysiology involves damage to peripheral and/or central auditory pathways. However, whether auditory system dysfunction is sufficient to explain chronic tinnitus is unclear, especially in light of evidence implicating other networks, including the limbic system. Using functional magnetic resonance imaging and voxel-based morphometry, we assessed tinnitus-related functional and anatomical anomalies in auditory and limbic networks. Moderate hyperactivity was present in the primary and posterior auditory cortices of tinnitus patients. However, the nucleus accumbens exhibited the greatest degree of hyperactivity, specifically to sounds frequency-matched to patients’ tinnitus. Complementary structural differences were identified in ventromedial prefrontal cortex, another limbic structure heavily connected to the nucleus accumbens. Furthermore, tinnitus-related anomalies were intercorrelated in the two limbic regions and between limbic and primary auditory areas, indicating the importance of auditory-limbic interactions in tinnitus.

Debates about motor theories of speech perception have recently been reignited by a burst of reports implicating premotor cortex (PMC) in speech perception. Often, however, these debates conflate perceptual and decision processes. Evidence that PMC activity correlates with task difficulty and subject performance suggests that PMC might be recruited, in certain cases, to facilitate category judgments about speech sounds (rather than speech perception, which involves decoding of sounds). However, it remains unclear whether PMC does, indeed, exhibit neural selectivity that is relevant for speech decisions. Further, it is unknown whether PMC activity in such cases reflects input via the dorsal or ventral auditory pathway, and whether PMC processing of speech is automatic or task-dependent. In a novel modified categorization paradigm, we presented human subjects with paired speech sounds from a phonetic continuum but diverted their attention from phoneme category using a challenging dichotic listening task. Using fMRI rapid adaptation to probe neural selectivity, we observed acoustic-phonetic selectivity in left anterior and left posterior auditory cortical regions. Conversely, we observed phoneme-category selectivity in left PMC that correlated with explicit phoneme-categorization performance measured after scanning, suggesting that PMC recruitment can account for performance on phoneme-categorization tasks. Structural equation modeling revealed connectivity from posterior, but not anterior, auditory cortex to PMC, suggesting a dorsal route for auditory input to PMC. Our results provide evidence for an account of speech processing in which the dorsal stream mediates automatic sensorimotor integration of speech and may be recruited to support speech decision tasks.

Converging evidence supports the hypothesis that an antero-lateral processing pathway mediates sound identification in auditory cortex, analogous to the role of the ventral cortical pathway in visual object recognition. Studies in nonhuman primates have characterized the antero-lateral auditory pathway as a processing hierarchy, composed of three anatomically and physiologically distinct initial stages: core, belt and parabelt. In humans, potential homologues of these regions have been identified anatomically, but reliable and complete functional distinctions between them have yet to be established. Because the anatomical locations of these fields vary across subjects, investigations of potential homologues between monkeys and humans require these fields to be defined in single subjects. Using functional MRI, we presented three classes of sounds (tones, band-passed noise bursts, and conspecific vocalizations), equivalent to those used in previous monkey studies. In each individual subject, three regions showing functional similarities to macaque core, belt and parabelt were readily identified. Furthermore, the relative sizes and locations of these regions were consistent with those reported in human anatomical studies. Our results demonstrate that the functional organization of the antero-lateral processing pathway in humans is largely consistent with that of nonhuman primates. Because our scanning sessions last only 15 min/subject, they can be run in conjunction with other scans. This will enable future studies to characterize functional modules in human auditory cortex at a level of detail previously possible only in visual cortex. Furthermore, the approach of employing identical schemes in both humans and monkeys will aid with establishing potential homologies between them.

Non-invasive neuroimaging studies have shown that semantic category and attribute information are encoded in neural population activity. Electrocorticography (ECoG) offers several advantages over non-invasive approaches, but the degree to which semantic attribute information is encoded in ECoG responses is not known. We recorded ECoG while patients named objects from 12 semantic categories and then trained high-dimensional encoding models to map semantic attributes to spectral-temporal features of the task-related neural responses. Using these semantic attribute encoding models, untrained objects were decoded with accuracies comparable to whole-brain functional Magnetic Resonance Imaging (fMRI), and we observed that high-gamma activity (70-110Hz) at basal occipitotemporal electrodes was associated with specific semantic dimensions (manmade-animate, canonically large-small, and places-tools). Individual patient results were in close agreement with reports from other imaging modalities on the time course and functional organization of semantic processing along the ventral visual pathway during object recognition. The semantic attribute encoding model approach is critical for decoding objects absent from a training set, as well as for studying complex semantic encodings without artificially restricting stimuli to a small number of semantic categories.

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Grouping auditory stimuli into common categories is essential for a variety of auditory tasks, including speech recognition. We trained human participants to categorize auditory stimuli from a large novel set of morphed monkey vocalizations. Using fMRI-rapid adaptation (fMRI-RA) and multi-voxel pattern analysis (MVPA) techniques, we gained evidence that categorization training results in two distinct sets of changes: sharpened tuning to monkey call features (without explicit category representation) in left auditory cortex and category selectivity for different types of calls in lateral prefrontal cortex. In addition, the sharpness of neural selectivity in left auditory cortex, as estimated with both fMRI-RA and MVPA, predicted the steepness of the categorical boundary, whereas categorical judgment correlated with release from adaptation in the left inferior frontal gyrus. These results support the theory that auditory category learning follows a two-stage model analogous to the visual domain, suggesting general principles of perceptual category learning in the human brain.

An open challenge at the forefront of modern neuroscience is to obtain a comprehensive mapping of the neural pathways that underlie human brain function; an enhanced understanding of the wiring diagram of the brain promises to lead to new breakthroughs in diagnosing and treating neurological disorders. Inferring brain structure from image data, such as that obtained via electron microscopy (EM), entails solving the problem of identifying biological structures in large data volumes. Synapses, which are a key communication structure in the brain, are particularly difficult to detect due to their small size and limited contrast. Prior work in automated synapse detection has relied upon time-intensive, error-prone biological preparations (isotropic slicing, post-staining) in order to simplify the problem. This paper presents VESICLE, the first known approach designed for mammalian synapse detection in anisotropic, non-poststained data. Our methods explicitly leverage biological context, and the results exceed existing synapse detection methods in terms of accuracy and scalability. We provide two different approaches -a deep learning classifier (VESICLE-CNN) and a lightweight Random Forest approach (VESICLE-RF), to offer alternatives in the performance-scalability space. Addressing this synapse detection challenge enables the analysis of high-throughput imaging that is soon expected to produce petabytes of data, and provides tools for more rapid estimation of brain-graphs. Finally, to facilitate community efforts, we developed tools for large-scale object detection, and demonstrated this framework to find ⇡ 50,000 synapses in 60,000 µm 3 (220 GB on disk) of electron microscopy data.Figure 1: Previous work on synapse detection has focused on isotropic post-stained data (left), which shows crisp membranes and dark fuzzy post synaptic densities (arrows) from all orientations. The alternative imaging technique of non post-stained, anisotropic data (middle, right) promises higher throughput, lack of staining artifacts, reduction in lost slices, and less demanding data storage requirements -all critically important for high-throughput connectomics. The XZ plane of a synapse in anisotropic data is shown (right), illustrating the effect of lower resolution. We address this more challenging environment, in which membranes appear fuzzier and are harder to distinguish from synaptic contacts. Data courtesy of Graham Knott (left) and Jeff Lichtman (middle, right).In Figure 1, we provide examples demonstrating the challenging setting of our detection task. Figure 2 illustrates the importance of leveraging contextual information, especially neurotransmitter-containing vesicles and cell membranes. Finally, Figure 3 presents a summary of classifier performance; visualizations of the results are not shown here, but are available in the full paper and on our website. Our code and data are open source and available at: openconnecto.me/vesicle. The detection task is to identify synapses shown in green (upper right). These synapses are kn...

Significance: Cerebral blood flow is an important biomarker of brain health and function as it regulates the delivery of oxygen and substrates to tissue and the removal of metabolic waste products. Moreover, blood flow changes in specific areas of the brain are correlated with neuronal activity in those areas. Diffuse correlation spectroscopy (DCS) is a promising noninvasive optical technique for monitoring cerebral blood flow and for measuring cortex functional activation tasks. However, the current state-of-the-art DCS adoption is hindered by a trade-off between sensitivity to the cortex and signal-to-noise ratio (SNR). Aim: We aim to develop a scalable method that increases the sensitivity of DCS instruments. Approach: We report on a multispeckle DCS (mDCS) approach that is based on a 1024-pixel single-photon avalanche diode (SPAD) camera. Our approach is scalable to >100;000 independent speckle measurements since large-pixel-count SPAD cameras are becoming available, owing to the investments in LiDAR technology for automotive and augmented reality applications. Results: We demonstrated a 32-fold increase in SNR with respect to traditional single-speckle DCS. Conclusion: A mDCS system that is based on a SPAD camera serves as a scalable method toward high-sensitivity DCS measurements, thus enabling both high sensitivity to the cortex and high SNR.