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In many experiments that investigate auditory and speech processing in the brain using electroencephalography (EEG), the experimental paradigm is often lengthy and tedious. Typically, the experimenter errs on the side of including more data, more trials, and therefore conducting a longer task to ensure that the data are robust and effects are measurable. Recent studies used naturalistic stimuli to investigate the brain's response to individual or a combination of multiple speech features using system identification techniques, such as multivariate temporal receptive field (mTRF) analyses. The neural data collected from such experiments must be divided into a training set and a test set to fit and validate the mTRF weights. While a good strategy is clearly to collect as much data as is feasible, it is unclear how much data are needed to achieve stable results. Furthermore, it is unclear whether the specific stimulus used for mTRF fitting and the choice of feature representation affects how much data would be required for robust and generalizable results. Here, we used previously collected EEG data from our lab using sentence stimuli and movie stimuli as well as EEG data from an open-source dataset using audiobook stimuli to better understand how much data needs to be collected for naturalistic speech experiments measuring acoustic and phonetic tuning. We found that the EEG receptive field structure tested here stabilizes after collecting a training dataset of approximately 200 s of TIMIT sentences, around 600 s of movie trailers training set data, and approximately 460 s of audiobook training set data. Thus, we provide suggestions on the minimum amount of data that would be necessary for fitting mTRFs from naturalistic listening data. Our findings are motivated by highly practical concerns when working with children, patient populations, or others who may not tolerate long study sessions. These findings will aid future researchers who wish to study naturalistic speech processing in healthy and clinical populations while minimizing participant fatigue and retaining signal quality.

In natural conversations, listeners must attend to what others are saying while ignoring extraneous background sounds. Recent studies have used encoding models to predict electroencephalography (EEG) responses to speech in noise-free listening situations, sometimes referred to as “speech tracking.” Researchers have analyzed how speech tracking changes with different types of background noise. It is unclear, however, whether neural responses from acoustically rich, naturalistic environments with and without background noise can be generalized to more controlled stimuli. If encoding models for acoustically rich, naturalistic stimuli are generalizable to other tasks, this could aid in data collection from populations of individuals who may not tolerate listening to more controlled and less engaging stimuli for long periods of time. We recorded noninvasive scalp EEG while 17 human participants (8 male/9 female) listened to speech without noise and audiovisual speech stimuli containing overlapping speakers and background sounds. We fit multivariate temporal receptive field encoding models to predict EEG responses to pitch, the acoustic envelope, phonological features, and visual cues in both stimulus conditions. Our results suggested that neural responses to naturalistic stimuli were generalizable to more controlled datasets. EEG responses to speech in isolation were predicted accurately using phonological features alone, while responses to speech in a rich acoustic background were more accurate when including both phonological and acoustic features. Our findings suggest that naturalistic audiovisual stimuli can be used to measure receptive fields that are comparable and generalizable to more controlled audio-only stimuli.SIGNIFICANCE STATEMENTUnderstanding spoken language in natural environments requires listeners to parse acoustic and linguistic information in the presence of other distracting stimuli. However, most studies of auditory processing rely on highly controlled stimuli with no background noise, or with background noise inserted at specific times. Here, we compare models where EEG data are predicted based on a combination of acoustic, phonetic, and visual features in highly disparate stimuli—sentences from a speech corpus and speech embedded within movie trailers. We show that modeling neural responses to highly noisy, audiovisual movies can uncover tuning for acoustic and phonetic information that generalizes to simpler stimuli typically used in sensory neuroscience experiments.

Speech perception is thought to rely on a cortical feedforward serial transformation of acoustic into linguistic representations. Using intracranial recordings across the entire human auditory cortex, electrocortical stimulation, and surgical ablation, we show that cortical processing across areas is not consistent with a serial hierarchical organization. Instead, response latency and receptive field analyses demonstrate parallel and distinct information processing in the primary and nonprimary auditory cortices. This functional dissociation was also observed where stimulation of the primary auditory cortex evokes auditory hallucination but does not distort or interfere with speech perception. Opposite effects were observed during stimulation of nonprimary cortex in superior temporal gyrus. Ablation of the primary auditory cortex does not affect speech perception. These results establish a distributed functional organization of parallel information processing throughout the human auditory cortex and demonstrate an essential independent role for nonprimary auditory cortex in speech processing.

The growing intervention of automated speech recognition applications in everyday life drives improvement in Brain-Computer Interfaces (BCI) for speech processing. By incorporating ASR (Automatic Speech Recognition)-based “keyword spotting” or “wake up commands”, we provide techniques for assessing when a BCI should start decoding, improving accuracy and efficiency for end users. Here, we use high density scalp EEG collected while participants listened to continuous speech in an audio-only, clear context, or while they watched highly noisy, naturalistic audiovisual movie clips. We designed three speech processing deep learning models: A sentence spotter (SS) model, Phoneme vs. Silence (PS) classifier and finally, Audio vs. Audio-visual (AV) stimuli induced EEG response classifier. The overall goal of this study is to design and examine the performance of these techniques for various speech processing applications. We use Long-Short Term Memory (LSTM) and Gated Recurrent Units (GRUs) neural network architectures and evaluated them on 16 participants' EEG data. We show 98.15% accuracy for our AV classifier, 98.56 F1 score on SS model and 70.33 F1 score on the PS model. 11Please refer to https://github.com/Madhusakth/BCI-Sentence-Silence-Spotter for the code.

OBJECTIVE Intracranial electroencephalography (ICEEG) recordings are performed for seizure localization in medically refractory epilepsy. Signal quantifications such as frequency power can be projected as heatmaps on personalized three-dimensional (3D) reconstructed cortical surfaces to distill these complex recordings into intuitive cinematic visualizations. However, simultaneously reconciling deep recording locations and reliably tracking evolving ictal patterns remain significant challenges. METHODS We fused oblique magnetic resonance imaging (MRI) slices along depth probe trajectories with cortical surface reconstructions and projected dynamic heatmaps using a simple mathematical metric of epileptiform activity (line-length). This omni-planar and surface casting of epileptiform activity approach (OPSCEA) thus illustrated seizure onset and spread among both deep and superficial locations simultaneously with minimal need for signal processing supervision. We utilized the approach on 41 patients at our center implanted with grid, strip, and/or depth electrodes for localizing medically refractory seizures. Peri-ictal data were converted into OPSCEA videos with multiple 3D brain views illustrating all electrode locations. Five people of varying expertise in epilepsy (medical student through epilepsy attending level) attempted to localize the seizure-onset zones. RESULTS We retrospectively compared this approach with the original ICEEG study reports for validation. Accuracy ranged from 73.2% to 97.6% for complete or overlapping onset lobe(s), respectively, and ~56.1% to 95.1% for the specific focus (or foci). Higher answer certainty for a given case predicted better accuracy, and scorers had similar accuracy across different training levels. SIGNIFICANCE In an era of increasing stereo-EEG use, cinematic visualizations fusing omni-planar and surface functional projections appear to provide a useful adjunct for interpreting complex intracranial recordings and subsequent surgery planning.

In natural conversations, listeners must attend to what others are saying while ignoring extraneous background sounds. Recent studies have used encoding models to predict electroencephalography (EEG) responses to speech in noise-free listening situations, sometimes referred to as “speech tracking” in EEG. Researchers have analyzed how speech tracking changes with different types of background noise. It is unclear, however, whether neural responses from noisy and naturalistic environments can be generalized to more controlled stimuli. If encoding models for noisy, naturalistic stimuli are generalizable to other tasks, this could aid in data collection from populations who may not tolerate listening to more controlled, less-engaging stimuli for long periods of time. We recorded non-invasive scalp EEG while participants listened to speech without noise and audiovisual speech stimuli containing overlapping speakers and background sounds. We fit multivariate temporal receptive field (mTRF) encoding models to predict EEG responses to pitch, the acoustic envelope, phonological features, and visual cues in both noise-free and noisy stimulus conditions. Our results suggested that neural responses to naturalistic stimuli were generalizable to more controlled data sets. EEG responses to speech in isolation were predicted accurately using phonological features alone, while responses to noisy speech were more accurate when including both phonological and acoustic features. These findings may inform basic science research on speech-in-noise processing. Ultimately, they may also provide insight into auditory processing in people who are hard of hearing, who use a combination of audio and visual cues to understand speech in the presence of noise.Significance StatementUnderstanding spoken language in natural environments requires listeners to parse acoustic and linguistic information in the presence of other distracting stimuli. However, most studies of auditory processing rely on highly controlled stimuli with no background noise, or with background noise inserted at specific times. Here, we compare models where EEG data are predicted based on a combination of acoustic, phonetic, and visual features in highly disparate stimuli – sentences from a speech corpus, and speech embedded within movie trailers. We show that modeling neural responses to highly noisy, audiovisual movies can uncover tuning for acoustic and phonetic information that generalizes to simpler stimuli typically used in sensory neuroscience experiments.

Speech perception involves the extraction of acoustic and phonological features from the speech signal. How those features map out across the human auditory cortex is unknown. Complementary to noninvasive imaging, the high spatial and temporal resolution of intracranial recordings has greatly contributed to recent advances in our understanding. However, these approaches are typically limited by piecemeal sampling of the expansive human temporal lobe auditory cortex. Here, we present a functional characterization of local cortical encoding throughout all major regions of the primary and non-primary human auditory cortex. We overcame previous limitations by using rare direct recordings from the surface of the temporal plane after surgical microdissection of the deep Sylvian fissure between the frontal and temporal lobes. We recorded neural responses using simultaneous high-density direct recordings over the left temporal plane and the lateral superior temporal gyrus, while participants listened to natural speech sentences and pure tone stimuli. We found an anatomical separation of simple spectral feature tuning, including tuning for pure tones and absolute pitch, on the superior surface of the temporal plane, and complex tuning for phonological features, relative pitch and speech amplitude modulations on lateral STG. Broadband onset responses are unique to posterior STG and not found elsewhere in auditory cortices. This onset region is functionally distinct from the rest of STG, with latencies similar to primary auditory areas. These findings reveal a new, detailed functional organization of response selectivity to acoustic and phonological features in speech throughout the human auditory cortex.

Humans have a unique ability to produce and consume rich, complex, and varied language in order to communicate ideas to one another. Still, outside of natural reading, the most common methods for studying how our brains process speech or understand language use only isolated words or simple sentences. Recent studies have upset this status quo by employing complex natural stimuli and measuring how the brain responds to language as it is used. In this article we argue that natural stimuli offer many advantages over simplified, controlled stimuli for studying how language is processed by the brain. Furthermore, the downsides of using natural language stimuli can be mitigated using modern statistical and computational techniques.

Background Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but is still superior to current rule-based approaches in most automated algorithms. Objective To develop an unsupervised machine-learning algorithm for the improved, automated detection and localization of interictal epileptiform discharges based on spatiotemporal pattern recognition. Methods We decomposed 24 h of intracranial electroencephalography signals into basis functions and activation vectors using non-negative matrix factorization (NNMF). Thresholding the activation vector and the basis function of interest detected interictal epileptiform discharges in time and space (specific electrodes), respectively. We used convolutive NNMF, a refined algorithm, to add a temporal dimension to basis functions. Results The receiver operating characteristics for NNMF-based detection are close to the gold standard of human visual-based detection and superior to currently available alternative automated approaches (93% sensitivity and 97% specificity). The algorithm successfully identified thousands of interictal epileptiform discharges across a full day of neurophysiological recording and accurately summarized their localization into a single map. Adding a temporal window allowed for visualization of the archetypal propagation network of these epileptiform discharges. Conclusion Unsupervised learning offers a powerful approach towards automated identification of recurrent pathological neurophysiological signals, which may have important implications for precise, quantitative, and individualized evaluation of focal epilepsy.

Speakers of all human languages regularly use intonational pitch to convey linguistic meaning, such as to emphasize a particular word. Listeners extract pitch movements from speech and evaluate the shape of intonation contours independent of each speaker's pitch range. We used high-density electrocorticography to record neural population activity directly from the brain surface while participants listened to sentences that varied in intonational pitch contour, phonetic content, and speaker. Cortical activity at single electrodes over the human superior temporal gyrus selectively represented intonation contours. These electrodes were intermixed with, yet functionally distinct from, sites that encoded different information about phonetic features or speaker identity. Furthermore, the representation of intonation contours directly reflected the encoding of speaker-normalized relative pitch but not absolute pitch.

The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

The principles underlying functional asymmetries in cortex remain debated. For example, it is accepted that speech is processed bilaterally in auditory cortex, but a left hemisphere dominance emerges when the input is interpreted linguistically. The mechanisms, however, are contested: what sound features or processing principles underlie laterality? Recent findings across species (humans, canines, bats) provide converging evidence that spectrotemporal sound features drive asymmetrical responses. Typically, accounts invoke models wherein the hemispheres differ in time-frequency resolution or integration window size. We develop a framework that builds on and unifies prevailing models, using spectrotemporal modulation space. Using signal processing techniques motivated by neural responses, we test this approach employing behavioral and neurophysiological measures. We show how psychophysical judgments align with spectrotemporal modulations and then characterize the neural sensitivities to temporal and spectral modulations. We demonstrate differential contributions from both hemispheres, with a left lateralization for temporal modulations and a weaker right lateralization for spectral modulations. We argue that representations in the modulation domain provide a more mechanistic basis to account for lateralization in auditory cortex.

Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measures of the living human brain. However, data collection is limited to highly specialized clinical environments. To improve internal (re)use and external sharing of these unique data, we present a structure for storing and sharing iEEG data: BIDS-iEEG, an extension of the Brain Imaging Data Structure (BIDS) specification, along with freely available examples and a bids-starter-kit. BIDS is a framework for organizing and documenting data and metadata with the aim to make datasets more transparent and reusable and to improve reproducibility of research. It is a community-driven specification with an inclusive decision-making process. As an extension of the BIDS specification, BIDS-iEEG facilitates integration with other modalities such as fMRI, MEG, and EEG. As the BIDS-iEEG extension has received input from many iEEG researchers, it provides a common ground for data transfer within labs, between labs, and in open-data repositories. It will facilitate reproducible analyses across datasets, experiments, and recording sites, allowing scientists to answer more complex questions about the human brain. Finally, the cross-modal nature of BIDS will enable efficient consolidation of data from multiple sites for addressing questions about generalized brain function.

The human superior temporal gyrus (STG) is critical for speech perception, yet the organization of spectrotemporal processing of speech within the STG is not well understood. Here, to characterize the spatial organization of spectrotemporal processing of speech across human STG, we use high-density cortical surface field potential recordings while participants listened to natural continuous speech. While synthetic broad-band stimuli did not yield sustained activation of the STG, spectrotemporal receptive fields could be reconstructed from vigorous responses to speech stimuli. We find that the human STG displays a robust anterior–posterior spatial distribution of spectrotemporal tuning in which the posterior STG is tuned for temporally fast varying speech sounds that have relatively constant energy across the frequency axis (low spectral modulation) while the anterior STG is tuned for temporally slow varying speech sounds that have a high degree of spectral variation across the frequency axis (high spectral modulation). This work illustrates organization of spectrotemporal processing in the human STG, and illuminates processing of ethologically relevant speech signals in a region of the brain specialized for speech perception.SIGNIFICANCE STATEMENTConsiderable evidence has implicated the human superior temporal gyrus (STG) in speech processing. However, the gross organization of spectrotemporal processing of speech within the STG is not well characterized. Here we use natural speech stimuli and advanced receptive field characterization methods to show that spectrotemporal features within speech are well organized along the posterior-to-anterior axis of the human STG. These findings demonstrate robust functional organization based on spectrotemporal modulation content, and illustrate that much of the encoded information in the STG represents the physical acoustic properties of speech stimuli.

To derive meaning from speech, we must extract multiple dimensions of concurrent information from incoming speech signals. That is, equally important to processing phonetic features is the detection of acoustic cues that give structure and context to the information we hear. How the brain organizes this information is unknown. Using data-driven computational methods on high-density intracranial recordings from 27 human participants, we reveal the functional distinction of neural responses to speech in the posterior superior temporal gyrus according to either onset or sustained response profiles. Though similar response types have been observed throughout the auditory system, we found novel evidence for a major spatial parcellation in which a distinct caudal zone detects acoustic onsets and a rostral-surround zone shows sustained, relatively delayed responses to ongoing speech stimuli. While posterior onset and anterior sustained responses are used substantially during natural speech perception, they are not limited to speech stimuli and are seen even for reversed or spectrally rotated speech. Single-electrode encoding of phonetic features in each zone depended upon whether the sound occurred at sentence onset, suggesting joint encoding of phonetic features and their temporal context. Onset responses in the caudal zone could accurately decode sentence and phrase onset boundaries, providing a potentially important internal mechanism for detecting temporal landmarks in speech and other natural sounds. These findings suggest that onset and sustained responses not only define the basic spatial organization of high-order auditory cortex but also have direct implications for how speech information is parsed in the cortex. VIDEO ABSTRACT.

SUMMARY The mammalian neocortex is a highly interconnected network of different types of neurons organized into both layers and columns. Overlaid on this structural organization is a pattern of functional connectivity that can be rapidly and flexibly altered during behavior. Parvalbumin-positive (PV+) inhibitory neurons, which are implicated in cortical oscillations and can change neuronal selectivity, may play a pivotal role in these dynamic changes. We found that optogenetic activation of PV+ neurons in the auditory cortex enhanced feedforward functional connectivity in the putative thalamorecipient circuit and in cortical columnar circuits. In contrast, stimulation of PV+ neurons induced no change in connectivity between sites in the same layers. The activity of PV+ neurons may thus serve as a gating mechanism to enhance feedforward, but not lateral or feedback, information flow in cortical circuits. Functionally, it may preferentially enhance the contribution of bottom-up sensory inputs to perception.

Attempts to relate the perceptual dimensions of timbre to quantitative acoustical dimensions have been tenuous, leading to claims that timbre is an emergent property, if measurable at all. Here, a three-pronged analysis shows that the timbre space of sustained instrument tones occupies 5 dimensions and that a specific combination of acoustic properties uniquely determines gestalt perception of timbre. Firstly, multidimensional scaling (MDS) of dissimilarity judgments generated a perceptual timbre space in which 5 dimensions were cross-validated and selected by traditional model comparisons. Secondly, subjects rated tones on semantic scales. A discriminant function analysis (DFA) accounting for variance of these semantic ratings across instruments and between subjects also yielded 5 significant dimensions with similar stimulus ordination. The dimensions of timbre space were then interpreted semantically by rotational and reflectional projection of the MDS solution into two DFA dimensions. Thirdly, to relate this final space to acoustical structure, the perceptual MDS coordinates of each sound were regressed with its joint spectrotemporal modulation power spectrum. Sound structures correlated significantly with distances in perceptual timbre space. Contrary to previous studies, most perceptual timbre dimensions are not the result of purely temporal or spectral features but instead depend on signature spectrotemporal patterns.

To derive meaning from speech, we must extract multiple dimensions of concurrent information from incoming speech signals, including phonetic and prosodic cues. Equally important is the detection of acoustic cues that give structure and context to the information we hear, such as sentence boundaries. How the brain organizes this information processing is unknown. Here, using data-driven computational methods on an extensive set of high-density intracranial recordings, we reveal a large-scale partitioning of the entire human speech cortex into two spatially distinct regions that detect important cues for parsing natural speech. These caudal (Zone 1) and rostral (Zone 2) regions work in parallel to detect onsets and prosodic information, respectively, within naturally spoken sentences. In contrast, local processing within each region supports phonetic feature encoding. These findings demonstrate a fundamental organizational property of the human auditory cortex that has been previously unrecognized.

The goals of this study were to first determine whether the fractional anisotropy (FA) and mean diffusivity (MD) of major white matter pathways associate with schizophrenia, and secondly to characterize the extent to which differences in these metrics might reflect a genetic predisposition to schizophrenia. Differences in FA and MD were identified using a comprehensive atlas-based tract mapping approach using diffusion tensor imaging and high-resolution structural data from 35 patients, 28 unaffected first-degree relatives of patients, 29 community controls, and 14 first-degree relatives of controls. Schizophrenia patients had significantly higher MD in the following tracts compared to controls: the right anterior thalamic radiations, the forceps minor, the bilateral inferior fronto-occipital fasciculus (IFO), the temporal component of the left superior longitudinal fasciculus (tSLF), and the bilateral uncinate. FA showed schizophrenia effects and a linear relationship to genetic liability (represented by schizophrenia patients, first-degree relatives, and controls) for the bilateral IFO, the left inferior longitudinal fasciculus (ILF), and the left tSLF. Diffusion tensor imaging studies have previously identified white matter abnormalities in all three of these tracts in schizophrenia; however, this study is the first to identify a significant genetic liability. Thus, FA of these three tracts may serve as biomarkers for studies seeking to identify how genes influence brain structure predisposing to schizophrenia. However, differences in FA and MD in frontal and temporal white matter pathways may be additionally driven by state variables that involve processes associated with the disease.

Cortical thickness reductions in prefrontal and temporal cortices have been repeatedly observed in patients with schizophrenia. However, it remains unclear whether regional variations in cortical thickness may be attributable to disease-related or genetic-liability factors.The structural magnetic resonance imaging data of 48 adult-onset schizophrenia patients, 66 first-degree non-psychotic relatives of schizophrenia patients, 27 community comparison (CC) probands and 77 CC relatives were examined using cortical pattern matching methods to map and compare highly localized changes in cortical gray matter thickness between groups defined by biological risk for schizophrenia.Schizophrenia patients showed marked cortical thinning primarily in frontal and temporal cortices when compared to unrelated CC probands. Results were similar, though less pronounced when patients were compared with their non-psychotic relatives. Cortical thickness reductions observed in unaffected relatives compared to age-similar CC relatives suggestive of schizophrenia-related genetic liability were marginal, surviving correction for the left parahippocampal gyrus and inferior occipital cortex only.Observations of pronounced fronto/temporal cortical thinning in schizophrenia patients replicate prior findings. The lack of marked cortical thickness alterations in non-psychotic relatives of patients, suggests that disease processes are primary contributors toward cortical thickness reductions in the disorder. However, genetic factors may have a larger influence on abnormalities in the medial temporal lobe.

The purpose of this study was to examine the effect of type 2 diabetes with major depression on cortical gray matter using magnetic resonance imaging and cortical pattern matching techniques. We hypothesized that diabetic subjects and depressed diabetic subjects would demonstrate decreased cortical gray matter thickness in prefrontal areas as compared to healthy control subjects. Patients with type 2 diabetes (n=26) and patients with diabetes and major depression (n=26) were compared with healthy controls (n=20). Gray matter thickness across the entire cortex was measured using cortical pattern matching methods. All subjects with diabetes demonstrated decreased cortical gray matter thickness in the left anterior cingulate region. Additionally, depressed diabetic subjects showed significant cortical gray matter decreases in bilateral prefrontal areas compared with healthy controls. Correlations between clinical variables and cortical gray matter thickness revealed a significant negative relationship with cerebrovascular risk factors across all three groups, most consistently in the left dorsomedial prefrontal cortex. A significant positive relationship between performance on attention and executive function tasks and cortical gray matter thickness predominantly in left hemisphere regions was also seen across all subjects. Depression and diabetes are associated with significant cortical gray matter thinning in medial prefrontal areas.

Diffusion tensor imaging (DTI) studies have demonstrated abnormal anisotropic diffusion in schizophrenia. However, examining data with low spatial resolution and/or a low number of gradient directions and limitations associated with analysis approaches sensitive to registration confounds may have contributed to mixed findings concerning the regional specificity and direction of results. This study examined three major white matter tracts connecting lateral and medial temporal lobe regions with neocortical association regions widely implicated in systems-level functional and structural disturbances in schizophrenia. Using DTIstudio, a previously validated regions of interest tractography method was applied to 30 direction diffusion weighted imaging data collected from demographically similar schizophrenia (n=23) and healthy control subjects (n=22). The diffusion tensor was computed at each voxel after intra-subject registration of diffusion-weighted images. Three-dimensional tract reconstruction was performed using the Fiber Assignment by Continuous Tracking (FACT) algorithm. Tractography results showed reduced fractional anisotropy (FA) of the arcuate fasciculi (AF) and inferior longitudinal fasciculi (ILF) in patients compared to controls. FA changes within the right ILF were negatively correlated with measures of thought disturbance. Reduced volume of the left AF was also observed in patients. These results, which avoid registration issues associated with voxel-based analyses of DTI data, support that fiber pathways connecting lateral and medial temporal lobe regions with neocortical regions are compromised in schizophrenia. Disruptions of connectivity within these pathways may potentially contribute to the disturbances of memory, language, and social cognitive processing that characterize the disorder.

Background Neuroimaging studies of attention-deficit/hyperactivity disorder (ADHD) have revealed structural abnormalities in the brains of affected individuals. One of the most replicated alterations is a significantly smaller corpus callosum (CC), for which conflicting reports exist with respect to the affected callosal segments. Methods We applied novel surface-based geometrical modeling methods to establish the presence, direction, and exact location of callosal alterations in ADHD at high spatial resolution. For this purpose, we calculated the thickness of the CC at 100 equidistant midsagittal points in an age-matched male sample of 19 individuals with ADHD and 19 typically developing control subjects. Results In close agreement with many prior observations, the CC was shown to be significantly thinner in ADHD subjects in anterior and, particularly, posterior callosal sections. Covarying for intelligence did not significantly alter the observed ADHD effects. However, group differences were no longer present in anterior sections when covarying for brain volume and after excluding ADHD subjects comorbid for oppositional defiant disorder. Conclusions Decreased callosal thickness may be associated with fewer fibers or a decrease in the myelination of fibers connecting the parietal and prefrontal cortices. This might affect interhemispheric communication channels that are necessary to sustain attention or motor control, thus contributing to symptoms of hyperactivity and impulsivity, or inattention, observed in ADHD. Future studies are necessary to determine whether callosal abnormalities reflect maturational delays or persist into adulthood.

Despite evidence for hippocampal abnormalities in elderly depression, it is unknown whether these changes are regionally specific. This study used three-dimensional mapping techniques to identify regional hippocampal abnormalities in early- and late-onset depression. Neuropsychological correlates of hippocampal morphology were also investigated.With high-resolution magnetic resonance imaging, hippocampal morphology was compared among elderly patients with early- (N=24) and late-onset (N=22) depression and comparison subjects (N=34). Regional structural abnormalities were identified by comparing distances, measured from homologous hippocampal surface points to the central core of each individual's hippocampal surface model, between groups.Hippocampal volumes differed between depressed patients and comparison subjects but not between patients with early- and late-onset depression. However, statistical mapping results showed that regional surface contractions were significantly pronounced in late- compared to early-onset depression in the anterior of the subiculum and lateral posterior of the CA1 subfield in the left hemisphere. Significant shape differences were observed bilaterally in anterior CA1-CA3 subfields and the subiculum in patients in relation to comparison subjects. These results were similar when each disease group was separately compared to comparison subjects. Hippocampal surface contractions significantly correlated with memory measures among late- but not early-onset depressed patients or comparison subjects.More pronounced regional volume deficits and their associations with memory in late-onset depression may suggest that these patients are more likely to develop cognitive impairment over time than individuals with early-onset depression. Mapping regional hippocampal abnormalities and their cognitive correlates may help guide research in defining risk profiles and treatment strategies.

Declarative memory (DM) impairments are reported in schizophrenia and in unaffected biological relatives of patients. However, the neural correlates of successful and unsuccessful encoding, mediated by the medial temporal lobe (MTL) memory system, and the influence of disease-related genetic liability remain under explored. This study employed an event-related functional MRI paradigm to compare activations for successfully and unsuccessfully encoded associative face-name stimuli between 26 schizophrenia patients (mean age: 33, 19 m/7f), 30 controls (mean age: 29, 24 m/6f), and 14 unaffected relatives of patients (mean age: 40, 5 m/9f). Compared to controls or unaffected relatives, patients showed hyper-activations in ventral visual stream and temporo-parietal cortical association areas when contrasting successfully encoded events to fixation. Follow-up hippocampal regions-of-interest analysis revealed schizophrenia-related hyper-activations in the right anterior hippocampus during successful encoding; contrasting successful versus unsuccessful events produced schizophrenia-related hypo-activations in the left anterior hippocampus. Similar hippocampal hypo-activations were observed in unaffected relatives during successful versus unsuccessful encoding. Post hoc analyses of hippocampal volume showed reductions in patients, but not in unaffected relatives compared to controls. Findings suggest that DM encoding deficits are attributable to both disease-specific and genetic liability factors that impact different components of the MTL memory system. Hyper-activations in temporo-occipital and parietal regions observed only in patients suggest the influence of disease-related factors. Regional hyper- and hypo-activations attributable to successful encoding occurring in both patients and unaffected relatives suggest the influence of schizophrenia-related genetic liability factors.

Author(s): Muller, Leah; Hamilton, Liberty S; Edwards, Erik; Bouchard, Kristofer E; Chang, Edward F | Abstract: ObjectiveElectrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram.ApproachFive subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient.Main resultsThe correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150 Hz) are largely uncorrelated (l90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies.SignificanceOur findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and across cortical areas during activity.

Dystrobrevin binding protein 1 (DTNBP1) has been identified as putative schizophrenia susceptibility gene, but it remains unknown whether polymorphisms relate to altered cerebral structure. We examined relationships between a previously implicated DTNBP1 risk variant [P1578] and global and segmented brain tissue volumes and regional cortical thickness in schizophrenia (n = 62; 24 risk carriers) and healthy subjects (n = 42; 11 risk carriers), across ethnic groups and within Caucasians. Schizophrenia patients showed similar brain volumes, but significantly reduced brain-size adjusted gray matter and CSF volumes and cortical thinning in a widespread neocortical distribution compared to controls. DTNBP1 risk was found associated with reduced brain volume, but not with tissue sub-compartments. Cortical thickness, which was weakly associated with brain size, showed regional variations in association with genetic risk, although effects were dominated by highly significant genotype by diagnosis interactions over broad areas of cortex. Risk status was found associated with regional cortical thinning in patients, particularly in temporal networks, but with thickness increases in controls. DTNBP1 effects for brain volume and cortical thickness appear driven by different neurobiological processes. Smaller brain volumes observed in risk carriers may relate to previously reported DTNBP1/cognitive function relationships irrespective of diagnosis. Regional cortical thinning in patient, but not in control risk carriers, may suggest that DTNBP1 interacts with other schizophrenia-related risk factors to affect laminar thickness. Alternatively, DTNBP1 may influence neural processes for which individuals with thicker cortex are less vulnerable. Although DTNBP1 relates to cortical thinning in schizophrenia, morphological changes in the disorder are influenced by additional genetic and/or environmental factors.

Mean diffusivity (MD), the rotationally invariant magnitude of water diffusion that is greater in cerebrospinal fluid (CSF) and smaller in organized brain tissue, has been suggested to reflect schizophrenia-associated cortical atrophy. Regional changes, associations with CSF, and the effects of genetic predisposition towards schizophrenia, however, remain uncertain. Six-direction diffusion tensor imaging DTI and high-resolution structural images were obtained from 26 schizophrenia patients, 36 unaffected first-degree patient relatives, 20 control subjects and 32 control relatives (N=114). Registration procedures aligned diffusion tensor imaging (DTI) data across imaging modalities. MD was averaged within lobar regions and the cingulate and superior temporal gyri. CSF volume and MD were highly correlated. Significant bilateral temporal, and superior temporal MD increases were observed in schizophrenia compared with unrelated control probands. First-degree relatives of schizophrenia probands showed larger MD measures compared with controls within bilateral superior temporal regions with CSF volume correction. Superior temporal lobe brain tissue deficits and proximal CSF enlargements are widely documented in schizophrenia. Larger MD indices in patients and their relatives may thus reflect similar pathophysiological mechanisms. However, persistence of regional MD effects after controlling for CSF volume, suggests that MD is a sensitive biological marker of disease and genetic liability, characterizing at least partially distinct aspects of brain structural integrity.

We used diffusion tensor imaging to investigate fractional anisotropy (FA), a measure of fiber tract integrity, in attention-deficit hyperactivity disorder (ADHD). Using a tract-based atlasing approach on six-direction diffusion tensor imaging data, we examined FA within the cingulum, corpus callosum, corticospinal tract, fornix, optic radiations, superior longitudinal fasciculus, uncinate fasciculus, and the superior and inferior occipitofrontal fasciculi in an all-male sample of 17 children and adolescents with ADHD and 16 age-matched controls. ADHD patients had significantly lower FA in the corticospinal tract (P=0.02) and the superior longitudinal fasciculus (P=0.017) compared with controls. Results support that disruptions in motor and attentional networks may contribute toward ADHD pathophysiology. Future research may clarify how ADHD subtype and psychiatric comorbidities affect diffusion measures.

The pronounced convolution of the human cortex may be a morphological substrate that supports some of our species’ most distinctive cognitive abilities. Therefore, individual intelligence within humans might be modulated by the degree of folding in certain cortical regions. We applied advanced methods to analyze cortical convolution at high spatial resolution and correlated those measurements with intelligence quotients. Within a large sample of healthy adult subjects (n = 65), we detected the most prominent correlations in the left medial hemisphere. More specifically, intelligence scores were positively associated with the degree of folding in the temporo-occipital lobe, particularly in the outermost section of the posterior cingulate gyrus (retrosplenial areas). Thus, this region might be an important contributor toward individual intelligence, either via modulating pathways to (pre)frontal regions or by serving as a location for the convergence of information. Prominent gender differences within the right frontal cortex were observed; females showed uncorrected significant positive correlations and males showed a nonsignificant trend toward negative correlations. It is possible that formerly described gender differences in regional convolution are associated with differences in the underlying architecture. This might lead to the development of sexually dimorphic information processing strategies and affect the relationship between intelligence and cortical convolution.

Sex, handedness, and disease processes in schizophrenia may affect the magnitude and/or direction of structural brain asymmetries. Using MRI data from 67 healthy (30 men, 10 nondextral) and 84 schizophrenia patients (60 men, 16 nondextral), cortical thickness asymmetries were compared at high spatial resolution. Within-group asymmetries were observed in sensorimotor, perisylvian, and parahippocampal cortices (leftward) and in anterior mesial frontal cortices (rightward). Asymmetry patterns were similar across diagnosis and sex, although some regional asymmetry increases were observed in men. Hand preference (dextrality) significantly influenced regional asymmetries in parietal association and dorsomedial frontal cortices (false discovery rate-corrected), where medial-frontal regions showed diagnosis by dextrality effects (uncorrected). Thus, dextrality relates to cortical thickness asymmetries, although schizophrenia may differentially affect asymmetry patterns across handedness.