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.

Objects seen as scenes: Neural circuitry for attending whole or parts.

Depending on our goals, we pay attention to the global shape of an object or to the local shape of its parts, since it's difficult to do both at once. This typically effortless process can be impaired in disease. However, it is not clear which cortical regions carry the information needed to constrain shape processing to a chosen global/local level. Here, novel stimuli were used to dissociate functional MRI responses to global and local shapes. This allowed identification of cortical regions containing information about level (independent from shape). Crucially, these regions overlapped part of the cortical network implicated in scene processing. As expected, shape information (independent of level) was mainly located in category-selective areas specialized for object- and face-processing. Regions with the same informational profile were strongly linked (as measured by functional connectivity), but were weak when the profiles diverged. Specifically, in the ventral-temporal-cortex (VTC) regions favoring level and shape were consistently separated by the mid-fusiform sulcus (MFS). These regions also had limited crosstalk despite their spatial proximity, thus defining two functional pathways within VTC. We hypothesize that object hierarchical level is processed by neural circuitry that also analyses spatial layout in scenes, contributing to the control of the spatial-scale used for shape recognition. Use of level information tolerant to shape changes could guide whole/part attentional selection but facilitate illusory shape/level conjunctions under impoverished vision.

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.

ERP Source Analysis Guided by fMRI During Familiar Face Processing.

Event related potentials (ERPs) provide precise temporal information about cognitive processing, but with poor spatial resolution, while functional magnetic resonance imaging (fMRI) reliably identifies brain areas involved, but with poor temporal resolution. Here we use fMRI to guide source localization of the ERPs at different times for studying the temporal dynamics of the neural system for recognizing familiar faces. fMRI activation areas were defined in a previous experiment applying the same paradigm used for ERPs. The Bayesian model averaging (BMA) method was used to estimate the generators of the ERPs to unfamiliar, visually familiar, and personally-familiar faces constraining the model by fMRI activation results. For this, higher prior probabilities in the solution space were assigned to the fMRI-defined regions, which included face-selective areas and other areas related to "person knowledge" retrieval. Source analysis was carried out in three-time windows: early (150-210 ms), middle (300-380 ms) and late (460-580 ms). The early and middle responses were generated in fMRI-defined areas for all face categories, while these areas do not contribute to the late response. Different areas contributed to the generation of the early and middle ERPs elicited by unfamiliar faces: fusiform (Fus), inferior occipital, superior temporal sulcus and the posterior cingulate (PC) cortices. For familiar faces, the contributing areas were Fus, PC and anterior temporal areas for visually familiar faces, with the addition of the medial orbitofrontal areas and other frontal structures for personally-significant faces. For both unfamiliar and familiar faces, more extended and reliable involvement of contributing areas were obtained for the middle compare with early time window. Our fMRI guide ERP source analysis suggested the recruitment of person-knowledge processing areas as early as 150-210 ms after stimulus onset during recognition of personally-familiar faces. We concluded that fMRI-constrained BMA source analysis provide information regarding the temporal-dynamics in the neural system for cognitive processsing.

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.

Fast Gaussian Naïve Bayes for searchlight classification analysis.

The searchlight technique is a variant of multivariate pattern analysis (MVPA) that examines neural activity across large sets of small regions, exhaustively covering the whole brain. This usually involves application of classifier algorithms across all searchlights, which entails large computational costs especially when testing the statistical significance of the accuracies with permutation methods. In this article, a new implementation of the Gaussian Naive Bayes classifier is presented (henceforth massive-GNB). This approach allows classification in all searchlights simultaneously, and is faster than previously published searchlight GNB implementations, as well as other more complex classifiers including support vector machines (SVM). To ensure that the gain in speed for GNB would be useful in searchlight analysis, we compared the accuracies of massive-GNB and SVM in detecting the lateral occipital complex (LOC) in an fMRI localizer experiment (26 subjects). Moreover, this region as defined in a meta-analysis of many activation studies was used as a gold standard to compare error rates for both classifiers. In individual searchlights, SVM was somewhat more accurate than massive-GNB and more selective in detecting the meta-analytic LOC. However, with multiple comparison correction at the cluster-level the two classifiers performed equivalently. Thus for cluster-level analysis, massive-GNB produces an accuracy similar to more sophisticated classifiers but with a substantial gain in speed. Massive-GNB (available as a public Matlab toolbox) could facilitate the more widespread use of searchlight analysis.

Sensation-to-cognition cortical streams in attention-deficit/hyperactivity disorder.

We sought to determine whether functional connectivity streams that link sensory, attentional, and higher-order cognitive circuits are atypical in attention-deficit/hyperactivity disorder (ADHD). We applied a graph-theory method to the resting-state functional magnetic resonance imaging data of 120 children with ADHD and 120 age-matched typically developing children (TDC). Starting in unimodal primary cortex-visual, auditory, and somatosensory-we used stepwise functional connectivity to calculate functional connectivity paths at discrete numbers of relay stations (or link-step distances). First, we characterized the functional connectivity streams that link sensory, attentional, and higher-order cognitive circuits in TDC and found that systems do not reach the level of integration achieved by adults. Second, we searched for stepwise functional connectivity differences between children with ADHD and TDC. We found that, at the initial steps of sensory functional connectivity streams, patients display significant enhancements of connectivity degree within neighboring areas of primary cortex, while connectivity to attention-regulatory areas is reduced. Third, at subsequent link-step distances from primary sensory cortex, children with ADHD show decreased connectivity to executive processing areas and increased degree of connections to default mode regions. Fourth, in examining medication histories in children with ADHD, we found that children medicated with psychostimulants present functional connectivity streams with higher degree of connectivity to regions subserving attentional and executive processes compared to medication-naïve children. We conclude that predominance of local sensory processing and lesser influx of information to attentional and executive regions may reduce the ability to organize and control the balance between external and internal sources of information in ADHD.

Mother adversity and co-residence time impact mother-child similarity in genome-wide and gene-specific methylation profiles.

BACKGROUND: The effects of adverse life events on physical and psychological health, with DNA methylation (DNAm) as a critical underlying mechanism, have been extensively studied. However, the epigenetic resemblance between mother and child in the context of neglectful caregiving, and whether it may be shaped by the emotional impact of maternal stressful events and the duration of co-residence (indexed by child age), remains unknown. The present study examined mother-child similarity in methylation profiles, considering the potential effect of mother adversity, mother empathy, neglect-control group, child age (an index of years of mother-child co-residence), and mother age. Using Illumina Epic arrays, we quantified DNAm in 115 mother-child saliva samples. We obtained a methylation similarity index by computing correlation coefficients between methylation profiles within dyads, for the entire epigenome, and five specific genes related to stress and empathy: NR3C1, FKPB5, OXTR, SCL6A4, and BDNF. RESULTS: The methylation profiles of the mother-child familial pairs significantly correlated as compared to mother-child random pairs for the entire epigenome and NR3C1, FKBP5, OXTR and BDNF genes. Next, multiple linear regression models observed associations of mother adversity, child age, and neglect-control group on mother-child methylation similarity, only significant in mother-child familial pairs, after correcting for multiple comparisons. Higher mother adversity was associated with lower mother-child methylation similarity for the epigenome-wide analysis, for the BDNF gene, and in the neglect-control group for the OXTR gene. In turn, being an older child (longer co-residence) was associated with higher mother-child methylation similarity. CONCLUSIONS: Mother adversity and co-residence time are modulating factors in the intergenerational methylation process that offer a window into development-dependent adaptations that can be affected by both hereditary and environmental factors, significantly observed only in biological dyads. A twofold implication for child well-being emerges, one is positive in that children of mothers exposed to life adversity or neglect did not necessarily inherit their methylation patterns. The other is concerning due to the influence of time spent living together, which affects similarity with the mother and potentially increases the risk of inheriting an epigenetic profile associated with future dysfunctional parenting patterns. This underscores the importance of the 'the earlier, the better' recommendation by the Child Protection System, which is not always followed.

Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC.

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

The Cuban Twin Registry: initial findings and perspectives.

The Cuban Twin Registry is a nation-wide, prospective, population-based twin registry comprising all zygosity types and ages. It was initiated in 2004 to study genetic and environmental contributions to complex diseases with high morbidity and mortality in the Cuban population. The database contains extensive information from 55,400 twin pairs enrolled in the period 2004-2006. Additionally, 2,600 new multiple births have been included from 2007 to date. In the past 4 years, more than 130 studies have been carried out using the registry with a classical genetic epidemiological approach in which concordance rates for monozygotic and dizygotic twins and heritability of various disease traits were estimated. This article summarizes the history, registry's methodology, recent research findings, and future directions of work.

Layer-specific correlates of detected and undetected auditory targets during attention.

In everyday life, the processing of acoustic information allows us to react to subtle changes in the auditory scene. Yet even when closely attending to sounds in the context of a task, we occasionally miss task-relevant features. The neural computations that underlie our ability to detect behavioral relevant sound changes are thought to be grounded in both feedforward and feedback processes within the auditory hierarchy. Here, we assessed the role of feedforward and feedback contributions in primary and non-primary auditory areas during behavioral detection of target sounds using submillimeter spatial resolution functional magnetic resonance imaging (fMRI) at high-fields (7 T) in humans. We demonstrate that the successful detection of subtle temporal shifts in target sounds leads to a selective increase of activation in superficial layers of primary auditory cortex (PAC). These results indicate that feedback signals reaching as far back as PAC may be relevant to the detection of targets in the auditory scene.

3D statistical parametric mapping of quiet sleep EEG in the first year of life.

This paper extends previously developed 3D SPM for Electrophysiological Source Imaging (Bosch et al., 2001) for neonate EEG. It builds on a prior paper by our group that established age dependent means and standard deviations for the scalp EEG Broad Band Spectral Parameters of children in the first year of life. We now present developmental equations for the narrow band log spectral power of EEG sources, obtained from a sample of 93 normal neonates from age 1 to 10 months in quiet sleep. The main finding from these regressions is that EEG power from 0.78 to 7.5 Hz decreases with age and also for 45-50 Hz. By contrast, there is an increase with age in the frequency band of 19-32 Hz localized to parietal, temporal and occipital areas. Deviations from the norm were analyzed for normal neonates and 17 with brain damage. The diagnostic accuracy (measured by the area under the ROC curve) of EEG source SPM is 0.80, 0.69 for average reference scalp EEG SPM, and 0.48 for Laplacian EEG SPM. This superior performance of 3D SPM over scalp qEEG suggests that it might be a promising approach for the evaluation of brain damage in the first year of life.

The shared mother-child epigenetic signature of neglect is related to maternal adverse events.

Studies of DNA methylation have revealed the biological mechanisms by which life adversity confers risk for later physical and mental health problems. What remains unknown is the "biologically embedding" of maternal adverse experiences resulting in maladaptive parenting and whether these epigenetic effects are transmitted to the next generation. This study focuses on neglectful mothering indexed by a severe disregard for the basic and psychological needs of the child. Using the Illumina Human Methylation EPIC BeadChip in saliva samples, we identified genes with differentially methylated regions (DMRs) in those mothers with (n = 51), versus those without (n = 87), neglectful behavior that present similar DMRs patterns in their children being neglected versus non-neglected (n = 40 vs. 75). Mothers reported the emotional intensity of adverse life events. After covariate adjustment and multiple testing corrections, we identified 69 DMRs in the mother epigenome and 42 DMRs in the child epigenome that were simultaneously above the α = 0.01 threshold. The common set of nine DMRs contained genes related to childhood adversity, neonatal and infant diabetes, child neurobehavioral development and other health problems such as obesity, hypertension, cancer, posttraumatic stress, and the Alzheimer's disease; four of the genes were associated with maternal life adversity. Identifying a shared epigenetic signature of neglect linked to maternal life adversity is an essential step in breaking the intergenerational transmission of one of the most common forms of childhood maltreatment.

Corticospinal tract and motor cortex degeneration in pure hereditary spastic paraparesis type 4 (SPG4).

Objective: SPG4 is an autosomal dominant pure form of hereditary spastic paraplegia (HSP) caused by mutations in the SPAST gene. HSP is considered an upper motor neuron disorder characterized by progressive retrograde degeneration, or "dying-back" phenomenon, of the corticospinal tract's longest axons. Neuroimaging studies mainly focus on white matter changes and, although previous studies reported cortical thinning in complicated HSP forms, cortical changes remain unclear in SPG4 patients. This work aimed to compare changes in white matter microstructure and cortical thickness between 12 SPG4 patients and 22 healthy age-matched controls. We also explore whether white matter alterations are related to cortical thickness and their correlation with clinical symptoms. Methods: we used fixel-based analysis, an advanced diffusion-weighted imaging technique, and probabilistic tractography of the corticospinal tracts. We also analyzed cortical morphometry using whole-brain surface-based and atlas-based methods in sensorimotor areas. Results: SPG4 patients showed bilateral involvement in the corticospinal tracts; this was more intense in the distal portion than in the upper segments and was associated with the degree of clinical impairment. We found a significant correlation between disease severity and fiber density and cross-section of the corticospinal tracts. Furthermore, corticospinal tract changes were significantly correlated with bilateral cortical thinning in the precentral gyrus in SPG4 patients. Conclusions: Our data point to axonal damage of the corticospinal motor neurons in SPG4 patients might be related to cortical thinning in motor regions.

Different and common brain signals of altered neurocognitive mechanisms for unfamiliar face processing in acquired and developmental prosopagnosia.

Neuropsychological studies have shown that prosopagnosic individuals perceive face structure in an atypical way. This might preclude the formation of appropriate face representations and, consequently, hamper effective recognition. The present ERP study, in combination with Bayesian source reconstruction, investigates how information related to both external (E) and internal (I) features was processed by E.C. and I.P., suffering from acquired and developmental prosopagnosia, respectively. They carried out a face-feature matching task with new faces. E.C. showed poor performance and remarkable lack of early face-sensitive P1, N170 and P2 responses on right (damaged) posterior cortex. Although she presented the expected mismatch effect to target faces in the E-I sequence, it was of shorter duration than in Controls, and involved left parietal, right frontocentral and dorsofrontal regions, suggestive of reduced neural circuitry to process face configurations. In turn, I.P. performed efficiently but with a remarkable bias to give "match" responses. His face-sensitive potentials P1-N170 were comparable to those from Controls, however, he showed no subsequent P2 response and a mismatch effect only in the I-E sequence, reflecting activation confined to those regions that sustain typically the initial stages of face processing. Relevantly, neither of the prosopagnosics exhibited conspicuous P3 responses to features acting as primes, indicating that diagnostic information for constructing face representations could not be sufficiently attended nor deeply encoded. Our findings suggest a different locus for altered neurocognitive mechanisms in the face network in participants with different types of prosopagnosia, but common indicators of a deficient allocation of attentional resources for further recognition.

Audiovisual Interactions Among Near-Threshold Oscillating Stimuli in the Far Periphery Are Phase-Dependent.

Recent studies have highlighted the possible contributions of direct connectivity between early sensory cortices to audiovisual integration. Anatomical connections between the early auditory and visual cortices are concentrated in visual sites representing the peripheral field of view. Here, we aimed to engage early sensory interactive pathways with simple, far-peripheral audiovisual stimuli (auditory noise and visual gratings). Using a modulation detection task in one modality performed at an 84% correct threshold level, we investigated multisensory interactions by simultaneously presenting weak stimuli from the other modality in which the temporal modulation was barely-detectable (at 55 and 65% correct detection performance). Furthermore, we manipulated the temporal congruence between the cross-sensory streams. We found evidence for an influence of barely-detectable visual stimuli on the response times for auditory stimuli, but not for the reverse effect. These visual-to-auditory influences only occurred for specific phase-differences (at onset) between the modulated audiovisual stimuli. We discuss our findings in the light of a possible role of direct interactions between early visual and auditory areas, along with contributions from the higher-order association cortex. In sum, our results extend the behavioral evidence of audio-visual processing to the far periphery, and suggest - within this specific experimental setting - an asymmetry between the auditory influence on visual processing and the visual influence on auditory processing.

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

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

White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm.

There are few studies on the neuroanatomical determinants of EEG spectral properties that would explain its substantial inter-individual variability in spite of decades of biophysical modeling that predicts this type of relationship. An exception is the negative relation between head size and the spectral position of the alpha peak (P(alpha)) reported in Nunez et al. (1978)-proposed as evidence of the influence of global boundary conditions on slightly damped neocortical waves. Here, we attempt to reexamine this finding by computing the correlations of occipital P(alpha) with various measures of head size and cortical surface area, for 222 subjects from the EEG/MRI database of the Cuban Human Brain Mapping Project. No relation is found (p>0.05). On the other hand, biophysical models also predict that white matter architecture, determining time delays and connectivities, could have an important influence on P(alpha). This led us to explore relations between P(alpha) and DTI fractional anisotropy by means of a multivariate penalized regression. Clusters of voxels with highly significant relations were found. These were positive within the Posterior and Superior Corona Radiata for both hemispheres, supporting biophysical theories predicting that the period of cortico-thalamocortical cycles might be modulating the alpha frequency. Posterior commissural fibers of the Corpus Callosum present the strongest relationships, negative in the inferior part (Splenium), connecting the inferior occipital lobes and positive in the superior part (Isthmus and Tapetum), connecting the superior occipital cortices. We found that white matter architecture rather than neocortical area determines the dynamics of the alpha rhythm.

False discovery rate and permutation test: an evaluation in ERP data analysis.

Current analysis of event-related potentials (ERP) data is usually based on the a priori selection of channels and time windows of interest for studying the differences between experimental conditions in the spatio-temporal domain. In this work we put forward a new strategy designed for situations when there is not a priori information about 'when' and 'where' these differences appear in the spatio-temporal domain, simultaneously testing numerous hypotheses, which increase the risk of false positives. This issue is known as the problem of multiple comparisons and has been managed with methods that control the false discovery rate (FDR), such as permutation test and FDR methods. Although the former has been previously applied, to our knowledge, the FDR methods have not been introduced in the ERP data analysis. Here we compare the performance (on simulated and real data) of permutation test and two FDR methods (Benjamini and Hochberg (BH) and local-fdr, by Efron). All these methods have been shown to be valid for dealing with the problem of multiple comparisons in the ERP analysis, avoiding the ad hoc selection of channels and/or time windows. FDR methods are a good alternative to the common and computationally more expensive permutation test. The BH method for independent tests gave the best overall performance regarding the balance between type I and type II errors. The local-fdr method is preferable for high dimensional (multichannel) problems where most of the tests conform to the empirical null hypothesis. Differences among the methods according to assumptions, null distributions and dimensionality of the problem are also discussed.