bluuebunny/crossref_metadata_2025_split huggingface dataset:
Visual Prediction Error Spreads Across Object Features in Human Visual Cortex
Authors: Jiang Jiefeng , Summerfield Christopher , Egner Tobias 
Published: November 2016

Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli. Behavioral data and neural signals from visual cortex were then interrogated to adjudicate between three possible ways in which prediction error (surprise) in the processing of one feature might affect the concurrent processing of another, expected feature: (1) feature processing may be independent; (2) surprise might “spread” from the unexpected to the expected feature, rendering the entire object unexpected; or (3) pairing a surprising feature with an expected feature might promote the inference that the two features are not in fact part of the same object. To formalize these rival hypotheses, we implemented them in a simple computational model of multifeature expectations. Across a range of analyses, behavior and visual neural signals consistently supported a model that assumes a mixing of prediction error signals across features: surprise in one object feature spreads to its other feature(s), thus rendering the entire object unexpected. These results reveal neurocomputational principles of multifeature expectations and indicate that objects are the unit of selection for predictive vision.SIGNIFICANCE STATEMENTWe address a key question in predictive visual cognition: how does the brain combine multiple concurrent expectations for different features of a single object such as its color and motion trajectory? By combining a behavioral protocol that independently varies expectation of (and attention to) multiple object features with computational modeling and fMRI, we demonstrate that behavior and fMRI activity patterns in visual cortex are best accounted for by a model in which prediction error in one object feature spreads to other object features. These results demonstrate how predictive vision forms object-level expectations out of multiple independent features.
Functional Organization of Flash-Induced V1 Offline Reactivation
Authors: Funayama Kenta , Hagura Nobuhiro , Ban Hiroshi , Ikegaya Yuji 
Published: November 2016

The primary visual cortex exhibits a late, long response with a latency of >300 ms and an immediate early response that occurs ∼100 ms after a visual stimulus. The late response is thought to contribute to visual functions such as sensory perception, iconic memory, working memory, and forming connections between temporally separated stimuli. However, how the visual late response is generated and organized is not completely understood. In the mouse primary visual cortexin vivo, we isolated long-delayed responses by using a brief light-flash stimulus for which the stimulus late response occurred long after the stimulus offset and was not contaminated by the instantaneous response evoked by the stimulus. Using whole-cell patch-clamp recordings, we demonstrated that the late rebound response was shaped by a net-balanced increase in excitatory and inhibitory synaptic conductances, whereas transient imbalances were caused by intermittent inhibitory barrage. In contrast to the common assumption that the neocortical late response reflects a feedback signal from the downstream higher-order cortical areas, our pharmacological and optogenetic analyses demonstrated that the late responses likely have a thalamic origin. Therefore, the late component of a sensory-evoked cortical response should be interpreted with caution.SIGNIFICANCE STATEMENTThe long-delayed responses of neocortical neurons are thought to arise from cortical feedback activity that is related to sensory perception and cognition. The mechanism of neocortical late responses was investigated using multiple electrophysiological techniques and the findings indicate that it actually arises from the thalamus. In addition, during the late response, excitation and inhibition are balanced, but inhibition is dominant in patterning action potentials.
Large Soluble Oligomers of Amyloid β-Protein from Alzheimer Brain Are Far Less Neuroactive Than the Smaller Oligomers to Which They Dissociate
Authors: Yang Ting , Li Shaomin , Xu Huixin , Walsh Dominic M. , Selkoe Dennis J. 
Published: November 2016

Soluble oligomers of amyloid β-protein (oAβ) isolated from the brains of Alzheimer's disease (AD) patients have been shown experimentally (in the absence of amyloid plaques) to impair hippocampal synaptic plasticity, decrease synapses, induce tau hyperphosphorylation and neuritic dystrophy, activate microglial inflammation, and impair memory in normal adult rodents. Nevertheless, there has been controversy about what types of oligomers actually confer these AD-like phenotypes. Here, we show that the vast majority of soluble Aβ species obtained from brains of humans who died with confirmed AD elute at high molecular weight (HMW) on nondenaturing size-exclusion chromatography. These species have little or no cytotoxic activity in several bioassays. However, incubation of HMW oAβ in mildly alkaline buffer led to their quantitative dissociation into low molecular weight oligomers (∼8–70 kDa), and these were now far more bioactive: they impaired hippocampal LTP, decreased neuronal levels of β2-adrenergic receptors, and activated microglia in wt micein vivo. Thus, most soluble Aβ assemblies in AD cortex are large and inactive but under certain circumstances can dissociate into smaller, highly bioactive species. Insoluble amyloid plaques likely sequester soluble HMW oligomers, limiting their potential to dissociate. We conclude that conditions that destabilize HMW oligomers or retard the sequestration of their smaller, more bioactive components are important drivers of Aβ toxicity. Selectively targeting these small, cytotoxic forms should be therapeutically beneficial.SIGNIFICANCE STATEMENTOligomers of amyloid β-protein (oAβ) are tought to play an important role in Alzheimer's disease (AD), but there is confusion and controversy about what types and sizes of oligomers have disease-relevant activity. Using size-exclusion chromatography and three distinct measures of bioactivity, we show that the predominant forms of Aβ in aqueous extracts of AD brain are high molecular weight (HMW) and relatively inactive. Importantly, under certain conditions, the abundant HMW oAβ can dissociate into low molecular weight species, and these low molecular weight oligomers are significantly more bioactive on synapses and microglia than the HMW species from which they are derived. We conclude that conditions that destabilize HMW oAβ or retard the sequestration of smaller, more bioactive components are important drivers of Aβ toxicity.
EEG Oscillations Are Modulated in Different Behavior-Related Networks during Rhythmic Finger Movements
Authors: Seeber Martin , Scherer Reinhold , Müller-Putz Gernot R. 
Published: November 2016

Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. We separated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for α (10–12 Hz) and β (18–24 Hz), but increase for high γ (60–80 Hz) frequencies during the entire movement period. Additionally, we found movement phase-related amplitudes, which resembled the flexion and extension sequence of the fingers. Especially for faster movement cadences, movement phase-related amplitudes included high β (24–30 Hz) frequencies in prefrontal areas. Interestingly, the spectral profiles and source patterns of movement phase-related amplitudes differed from sustained activities, suggesting that they represent different frequency-specific large-scale networks. First, networks were signified by the sustained element, which statically modulate their synchrony levels during continuous movements. These networks may upregulate neuronal excitability in brain regions specific to the limb, in this study the right hand area. Second, movement phase-related networks, which modulate their synchrony in relation to the movement sequence. We suggest that these frequency-specific networks are associated with distinct functions, including top-down control, sensorimotor prediction, and integration. The separation of different large-scale networks, we applied in this work, improves the interpretation of EEG sources in relation to human motor behavior.SIGNIFICANCE STATEMENTEEG recordings provide high temporal resolution suitable to relate cortical oscillations to actual movements. Investigating EEG sources during rhythmic finger movements, we distinguish sustained from movement phase-related amplitude modulations. We separate these two EEG source elements motivated by our previous findings in gait. Here, we found two types of large-scale networks, representing the right fingers in distinction from the time sequence of the movements. These findings suggest that EEG source amplitudes reconstructed in a cortical patch are the superposition of these simultaneously present network activities. Separating these frequency-specific networks is relevant for studying function and possible dysfunction of the cortical sensorimotor system in humans as well as to provide more advanced features for brain-computer interfaces.
Hippocampal Mismatch Signals Are Modulated by the Strength of Neural Predictions and Their Similarity to Outcomes
Authors: Long Nicole M. , Lee Hongmi , Kuhl Brice A. 
Published: November 2016

The hippocampus is thought to compare predicted events with current perceptual input, generating a mismatch signal when predictions are violated. However, most prior studies have only inferred when predictions occur without measuring them directly. Moreover, an important but unresolved question is whether hippocampal mismatch signals are modulated by the degree to which predictions differ from outcomes. Here, we conducted a human fMRI study in which subjects repeatedly studied various word–picture pairs, learning to predict particular pictures (outcomes) from the words (cues). After initial learning, a subset of cues was paired with a novel, unexpected outcome, whereas other cues continued to predict the same outcome. Critically, when outcomes changed, the new outcome was either “near” to the predicted outcome (same visual category as the predicted picture) or “far” from the predicted outcome (different visual category). Using multivoxel pattern analysis, we indexed cue-evoked reactivation (prediction) within neocortical areas and related these trial-by-trial measures of prediction strength to univariate hippocampal responses to the outcomes. We found that prediction strength positively modulated hippocampal responses to unexpected outcomes, particularly when unexpected outcomes were close, but not identical, to the prediction. Hippocampal responses to unexpected outcomes were also associated with a tradeoff in performance during a subsequent memory test: relatively faster retrieval of new (updated) associations, but relatively slower retrieval of the original (older) associations. Together, these results indicate that hippocampal mismatch signals reflect a comparison between active predictions and current outcomes and that these signals are most robust when predictions are similar, but not identical, to outcomes.SIGNIFICANCE STATEMENTAlthough the hippocampus is widely thought to signal “mismatches” between memory-based predictions and outcomes, previous research has not linked hippocampal mismatch signals directly to neural measures of prediction strength. Here, we show that hippocampal mismatch signals increase as a function of the strength of predictions in neocortical regions. This increase in hippocampal mismatch signals was particularly robust when outcomes were similar, but not identical, to predictions. These results indicate that hippocampal mismatch signals are driven by both the active generation of predictions and the similarity between predictions and outcomes.
Left Anterior Temporal Lobe and Bilateral Anterior Cingulate Cortex Are Semantic Hub Regions: Evidence from Behavior-Nodal Degree Mapping in Brain-Damaged Patients
Authors: Zhao Ying , Song Luping , Ding Junhua , Lin Nan , Wang Qiang , Du Xiaoxia , Sun Rong , Han Zaizhu 
Published: November 2016

The organizational principles of semantic memory in the human brain are still controversial. Although studies have shown that the semantic system contains hub regions that bind information from different sensorimotoric modalities to form concepts, it is unknown whether there are hub regions other than the anterior temporal lobe (ATL). Meanwhile, previous studies have rarely used network measurements to explore the hubs or correlated network indexes with semantic performance, although the most direct supportive evidence of hubs should come from the network perspective. To fill this gap, we correlated the brain-network index with semantic performance in 86 brain-damaged patients. We especially selected the nodal degree measure that reflects how well a node is connected in the network. The measure was calculated as the total number of connections of a given node with other nodes in the resting-state functional MRI network. Semantic ability was measured using the performance of both general and modality-specific (object form, color, motion, sound, manipulation, and function) semantic tasks. We found that the left ATL and the bilateral anterior cingulate cortex could be semantic hubs because the reduced nodal degree values of these regions could effectively predict the deficits in both general and modality-specific semantic performance. Moreover, the effects remained when the analyses were performed only in the patients who did not have lesions in these regions. The two hub regions might support semantic representations and executive control processes, respectively. These data provide empirical evidence for the distributed-plus-hub theory of semantic memory from the network perspective.SIGNIFICANCE STATEMENTAlthough the distributed-plus-hub organization of semantic memory has been proposed for several years, it remains unclear which hubs other than the anterior temporal lobe are included in the semantic system. Here, we identified such hubs from an innovative network perspective. The voxelwise nodal degree values were correlated with the performance of general and modality-specific semantic tasks in 86 patients with brain damage. We observed that the left anterior temporal lobe and bilateral anterior cingulate cortex could be semantic hubs because their decreased nodal degree values were significantly correlated with the severity of the deficit in semantic performance. The two hub regions might contribute to semantic representational and control processes, respectively. These findings offer new evidence for the distributed-plus-hub theory.
Dorsolateral Prefrontal Cortex GABA Concentration in Humans Predicts Working Memory Load Processing Capacity
Authors: Yoon Jong H. , Grandelis Anthony , Maddock Richard J. 
Published: November 2016

The discovery of neural mechanisms of working memory (WM) would significantly enhance our understanding of complex human behaviors and guide treatment development for WM-related impairments found in neuropsychiatric conditions and aging. Although the dorsolateral prefrontal cortex (DLPFC) has long been considered critical for WM, we still know little about the neural elements and pathways within the DLPFC that support WM in humans. In this study, we tested whether an individual's DLPFC gamma-aminobutryic acid (GABA) content predicts individual differences in WM task performance using a novel behavioral approach. Twenty-three healthy adults completed a task that measured the unique contribution of major WM components (memory load, maintenance, and distraction resistance) to performance. This was done to address the possibility that components have differing GABA dependencies and the failure to parse WM into components would lead to missing true associations with GABA. The subjects then had their DLPFC GABA content measured by single-voxel proton magnetic spectroscopy. We found that individuals with lower DLPFC GABA showed greater performance degradation with higher load, accounting for 31% of variance,p(corrected)= 0.015. This relationship was component, neurochemical, and brain region specific. DLPFC GABA content did not predict performance sensitivity to other components tested; DLPFC glutamate + glutamine and visual cortical GABA content did not predict load sensitivity. These results confirm the involvement of DLPFC GABA in WM load processing in humans and implicate factors controlling DLPFC GABA content in the neural mechanisms of WM and its impairments.SIGNIFICANCE STATEMENTThis study demonstrated for the first time that the amount of gamma-aminobutryic acid (GABA), the major inhibitory neurotransmitter of the brain, in an individual's prefrontal cortex predicts working memory (WM) task performance. Given that WM is required for many of the most characteristic cognitive and behavioral capabilities in humans, this finding could have a significant impact on our understanding of the neural basis of complex human behavior. Furthermore, this finding suggests that efforts to preserve or increase brain GABA levels could be fruitful in remediating WM-related deficits associated with neuropsychiatric conditions.
An Array of Descending Visual Interneurons Encoding Self-Motion inDrosophila
Authors: Suver Marie P. , Huda Ainul , Iwasaki Nicole , Safarik Steve , Dickinson Michael H. 
Published: November 2016

The means by which brains transform sensory information into coherent motor actions is poorly understood. In flies, a relatively small set of descending interneurons are responsible for conveying sensory information and higher-order commands from the brain to motor circuits in the ventral nerve cord. Here, we describe three pairs of genetically identified descending interneurons that integrate information from wide-field visual interneurons and project directly to motor centers controlling flight behavior. We measured the physiological responses of these three cells during flight and found that they respond maximally to visual movement corresponding to rotation around three distinct body axes. After characterizing the tuning properties of an array of nine putative upstream visual interneurons, we show that simple linear combinations of their outputs can predict the responses of the three descending cells. Last, we developed a machine vision-tracking system that allows us to monitor multiple motor systems simultaneously and found that each visual descending interneuron class is correlated with a discrete set of motor programs.SIGNIFICANCE STATEMENTMost animals possess specialized sensory systems for encoding body rotation, which they use for stabilizing posture and regulating motor actions. In flies and other insects, the visual system contains an array of specialized neurons that integrate local optic flow to estimate body rotation during locomotion. However, the manner in which the output of these cells is transformed by the downstream neurons that innervate motor centers is poorly understood. We have identified a set of three visual descending neurons that integrate the output of nine large-field visual interneurons and project directly to flight motor centers. Our results provide new insight into how the sensory information that encodes body motion is transformed into a code that is appropriate for motor actions.
Hermes Regulates Axon Sorting in the Optic Tract by Post-Trancriptional Regulation of Neuropilin 1
Authors: Hörnberg Hanna , Cioni Jean-Michel , Harris William A. , Holt Christine E. 
Published: November 2016

The establishment of precise topographic maps during neural development is facilitated by the presorting of axons in the pathway before they reach their targets. In the vertebrate visual system, such topography is seen clearly in the optic tract (OT) and in the optic radiations. However, the molecular mechanisms involved in pretarget axon sorting are poorly understood. Here, we show in zebrafish that the RNA-binding protein Hermes, which is expressed exclusively in retinal ganglion cells (RGCs), is involved in this process. Using a RiboTag approach, we show that Hermes acts as a negative translational regulator of specific mRNAs in RGCs. One of these targets is the guidance cue receptor Neuropilin 1 (Nrp1), which is sensitive to the repellent cue Semaphorin 3A (Sema3A). Hermes knock-down leads to topographic missorting in the OT through the upregulation of Nrp1. Restoring Nrp1 to appropriate levels in Hermes-depleted embryos rescues this effect and corrects the axon-sorting defect in the OT. Our data indicate that axon sorting relies on Hermes-regulated translation of Nrp1.SIGNIFICANCE STATEMENTAn important mechanism governing the formation of the mature neural map is pretarget axon sorting within the sensory tract; however, the molecular mechanisms involved in this process remain largely unknown. The work presented here reveals a novel function for the RNA-binding protein Hermes in regulating the topographic sorting of retinal ganglion cell (RGC) axons in the optic tract and tectum. We find that Hermes negatively controls the translation of the guidance cue receptor Neuropilin-1 in RGCs, with Hermes knock-down resulting in aberrant growth cone cue sensitivity and axonal topographic misprojections. We characterize a novel RNA-based mechanism by which axons restrict their translatome developmentally to achieve proper targeting.