NeuroImage

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ISSN / EISSN : 1053-8119 / 1095-9572
Published by: Elsevier BV (10.1016)
Total articles ≅ 24,810
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, Joon H. Paik
Published: 15 July 2021
NeuroImage, Volume 235, pp 117887-117887; doi:10.1016/j.neuroimage.2021.117887

Abstract:
Speech perception entails the mapping of the acoustic waveform to linguistic representations. For this transformation to succeed, the speech signal needs to be tracked over various temporal windows at high temporal precision in order to decode linguistic units ranging from phonemes (tens of milliseconds) to sentences (seconds). Here, we tested the hypothesis that cortical processing of speech-specific temporal structure is modulated by higher-level linguistic analysis. Using fMRI, we measured BOLD signal changes to 4 s long speech quilts with variable temporal structure (30, 120, 480, 960 ms segment lengths), as well as natural speech, created from a familiar (English) or foreign (Korean) language. We found evidence for the acoustic analysis of temporal speech properties in superior temporal sulcus (STS): the BOLD signal increased as a function of temporal speech structure in both familiar and foreign languages. However, activity in left inferior gyrus (IFG) revealed evidence for linguistic processing of temporal speech properties: the BOLD signal increased as a function of temporal speech structure only in familiar, but not in foreign speech. Network connectivity analyses suggested that left IFG modulates the processing of temporal speech structure in primary and non-primary auditory cortex, which in turn sensitizes the analysis of temporal speech structure in STS. The results thus suggest that acousto-linguistic transformation of temporal speech structure is achieved by a cortical network comprising primary and non-primary auditory cortex, STS, and left IFG.
, , , Marius de Groot, M. Arfan Ikram, ,
Published: 15 July 2021
NeuroImage, Volume 235; doi:10.1016/j.neuroimage.2021.118004

Abstract:
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and optimized simultaneously for their mutual benefit. An objective function that optimizes spatial correspondence for the segmented structures across time-points is proposed. We applied Segis-Net to the analysis of white matter tracts from N=8045 longitudinal brain MRI datasets of 3249 elderly individuals. Segis-Net approach showed a significant increase in registration accuracy, spatio-temporal segmentation consistency, and reproducibility compared with two multistage pipelines. This also led to a significant reduction in the sample-size that would be required to achieve the same statistical power in analyzing tract-specific measures. Thus, we expect that Segis-Net can serve as a new reliable tool to support longitudinal imaging studies to investigate macro- and microstructural brain changes over time.
, Jie Wen, Satya V.V.N. Kothapalli, Alexander L Sukstanskii
Published: 15 July 2021
NeuroImage, Volume 235; doi:10.1016/j.neuroimage.2021.118012

Abstract:
Non-heme iron is an important element supporting the structure and functioning of biological tissues. Imbalance in non-heme iron can lead to different neurological disorders. Several MRI approaches have been developed for iron quantification relying either on the relaxation properties of MRI signal or measuring tissue magnetic susceptibility. Specific quantification of the non-heme iron can, however, be constrained by the presence of the heme iron in the deoxygenated blood and contribution of cellular composition. The goal of this paper is to introduce theoretical background and experimental MRI method allowing disentangling contributions of heme and non-heme irons simultaneously with evaluation of tissue neuronal density in the iron-rich basal ganglia. Our approach is based on the quantitative Gradient Recalled Echo (qGRE) MRI technique that allows separation of the total R2* metric characterizing decay of GRE signal into tissue-specific (R2t*) and the baseline blood oxygen level-dependent (BOLD) contributions. A combination with the QSM data (also available from the qGRE signal phase) allowed further separation of the tissue-specific R2t* metric in a cell-specific and non-heme-iron-specific contributions. It is shown that the non-heme iron contribution to R2t* relaxation can be described with the previously developed Gaussian Phase Approximation (GPA) approach. qGRE data were obtained from 22 healthy control participants (ages 26–63 years). Results suggest that the ferritin complexes are aggregated in clusters with an average radius about 100nm comprising approximately 2600 individual ferritin units. It is also demonstrated that the concentrations of heme and non-heme iron tend to increase with age. The strongest age effect was seen in the pallidum region, where the highest age-related non-heme iron accumulation was observed.
, , Zoe Steine-Hanson, Natalie Koh, John E. Laird, Christian J. Lebiere, Paul Rosenbloom
Published: 15 July 2021
NeuroImage, Volume 235; doi:10.1016/j.neuroimage.2021.118035

Abstract:
The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains.
, Wietske Zuiderbaan, Benedetta Heimler, ,
Published: 15 July 2021
NeuroImage, Volume 235; doi:10.1016/j.neuroimage.2021.118029

Abstract:
Topographic maps, a key principle of brain organization, emerge during development. It remains unclear, however, whether topographic maps can represent a new sensory experience learned in adulthood. MaMe, a congenitally blind individual, has been extensively trained in adulthood for perception of a 2D auditory-space (soundscape) where the y- and x-axes are represented by pitch and time, respectively. Using population receptive field mapping we found neural populations tuned topographically to pitch, not only in the auditory cortices but also in the parietal and occipito-temporal cortices. Topographic neural tuning to time was revealed in the parietal and occipito-temporal cortices. Some of these maps were found to represent both axes concurrently, enabling MaMe to represent unique locations in the soundscape space. This case study provides proof of concept for the existence of topographic maps tuned to the newly learned soundscape dimensions. These results suggest that topographic maps can be adapted or recycled in adulthood to represent novel sensory experiences.
, Tatsuya Mima, Sumiya Shibata, Hikari Kirimoto
Published: 15 July 2021
NeuroImage, Volume 235; doi:10.1016/j.neuroimage.2021.118022

Abstract:
Control of movements using visual information is crucial for many daily activities, and such visuomotor control has been revealed to be supported by alpha and beta cortical oscillations. However, it has been remained to be unclear how midfrontal theta and occipital gamma oscillations, which are associated with high-level cognitive functions, would be involved in this process to facilitate performance. Here we addressed this fundamental open question in healthy young adults by measuring high-density cortical activity during a precision force-matching task. We manipulated the amount of error by changing visual feedback gain (low, medium, and high visual gains) and analyzed event-related spectral perturbations. Increasing the visual feedback gain resulted in a decrease in force error and variability. There was an increase in theta synchronization in the midfrontal area and also in beta desynchronization in the sensorimotor and posterior parietal areas with higher visual feedback gains. Gamma de/synchronization was not evident during the task. In addition, we found a moderation effect of midfrontal theta on the positive relationship between the beta oscillations and force error. Subsequent simple slope analysis indicated that the effect of beta oscillations on force error was weaker when midfrontal theta was high. Our findings suggest that the midfrontal area signals the increased need of cognitive control to refine behavior by modulating the visuomotor processing at theta frequencies.
, Valentina Borghesani, Manuela Piazza
Published: 15 July 2021
NeuroImage, Volume 235; doi:10.1016/j.neuroimage.2021.118016

Abstract:
When primates (both human and non-human) learn to categorize simple visual or acoustic stimuli by means of non-verbal matching tasks, two types of changes occur in their brain: early sensory cortices increase the precision with which they encode sensory information, and parietal and lateral prefrontal cortices develop a categorical response to the stimuli. Contrary to non-human animals, however, our species mostly constructs categories using linguistic labels. Moreover, we naturally tend to define categories by means of multiple sensory features of the stimuli. Here we trained adult subjects to parse a novel audiovisual stimulus space into 4 orthogonal categories, by associating each category to a specific symbol. We then used multi-voxel pattern analysis (MVPA) to show that during a cross-format category repetition detection task three neural representational changes were detectable. First, visual and acoustic cortices increased both precision and selectivity to their preferred sensory feature, displaying increased sensory segregation. Second, a frontoparietal network developed a multisensory object-specific response. Third, the right hippocampus and, at least to some extent, the left angular gyrus, developed a shared representational code common to symbols and objects. In particular, the right hippocampus displayed the highest level of abstraction and generalization from a format to the other, and also predicted symbolic categorization performance outside the scanner. Taken together, these results indicate that when humans categorize multisensory objects by means of language the set of changes occurring in the brain only partially overlaps with that described by classical models of non-verbal unisensory categorization in primates.
, Fon Powell
Published: 15 July 2021
NeuroImage, Volume 235; doi:10.1016/j.neuroimage.2021.118008

Abstract:
Huntington's Disease (HD), an autosomal dominant genetic disorder caused by a mutation in the Huntingtin gene (HTT), displays a stereotyped topography in the human brain and a stereotyped progression, initially appearing in the striatum. Like other degenerative diseases, spatial topography of HD is divorced from where implicated genes are expressed, a dissociation whose mechanistic underpinning is not currently understood. Cell autonomous molecular factors characterized by gene expression signatures, including proteolytic and post translational modifications, play a role in vulnerability to disease. Non-autonomous mechanisms, likely involving the brain's anatomic or functional connectivity patterns, might also be responsible for selective vulnerability in HD. Leveraging a large dataset of 635 subjects from a multinational study, this paper tests various cell-autonomous and non-autonomous models that can explain HD topography. We test whether the expression patterns of implicated genes is sufficient to explain regional HD atrophy, or whether the network transmission of protein products is required to explain them. We find that network models are capable of predicting, to a high degree, observed atrophy in human subjects. Lastly, we propose a model of anterograde network transmission, and show that it is the most parsimonious yet most likely to explain observed atrophy patterns in HD. Collectively, these data indicate that pathology spread in HD may be mediated by the brain's intrinsic structural network organization. This is the first study to systematically and quantitatively test multiple hypotheses of pathology spread in living human subjects with HD.
Meng Du, Ruby Basyouni,
Published: 15 July 2021
NeuroImage, Volume 235; doi:10.1016/j.neuroimage.2021.118019

Abstract:
How does the human brain support reasoning about social relations (e.g., social status, friendships)? Converging theories suggest that navigating knowledge of social relations may co-opt neural circuitry with evolutionarily older functions (e.g., shifting attention in space). Here, we analyzed multivoxel response patterns of fMRI data to examine the neural mechanisms for shifting attention in knowledge of a social hierarchy. The “directions” in which participants mentally navigated social knowledge were encoded in multivoxel patterns in superior parietal cortex, which also encoded directions of attentional shifts in space. Exploratory analyses implicated additional regions of posterior parietal and occipital cortex in encoding analogous mental operations in space and social knowledge. However, cross-domain analyses suggested that attentional shifts in space and social knowledge are likely encoded in functionally independent response patterns. Additionally, cross-participant multivoxel pattern similarity analyses indicated that “directions'' of mental navigation in social knowledge are signaled consistently across participants and across different social hierarchies in a set of brain regions, including the right superior parietal lobule. Taken together, these results elucidate the neural basis of navigating abstract knowledge of social relations, and its connection to more basic mental operations.
Nathalie Richard, Michel Desmurget, Achille Teillac, Pierre-Aurélien Beuriat, , Gino Coudé, Alexandru Szathmari, Carmine Mottolese, Angela Sirigu,
Published: 15 July 2021
NeuroImage, Volume 235; doi:10.1016/j.neuroimage.2021.118002

Abstract:
The dorso-posterior parietal cortex (DPPC) is a major node of the grasp/manipulation control network. It is assumed to act as an optimal forward estimator that continuously integrates efferent outflows and afferent inflows to modulate the ongoing motor command. In agreement with this view, a recent per-operative study, in humans, identified functional sites within DPPC that: (i) instantly disrupt hand movements when electrically stimulated; (ii) receive short-latency somatosensory afferences from intrinsic hand muscles. Based on these results, it was speculated that DPPC is part of a rapid grasp control loop that receives direct inputs from the hand-territory of the primary somatosensory cortex (S1) and sends direct projections to the hand-territory of the primary motor cortex (M1). However, evidence supporting this hypothesis is weak and partial. To date, projections from DPPC to M1 grasp zone have been identified in monkeys and have been postulated to exist in humans based on clinical and transcranial magnetic studies. This work uses diffusion-MRI tractography in two samples of right- (n = 50) and left-handed (n = 25) subjects randomly selected from the Human Connectome Project. It aims to determine whether direct connections exist between DPPC and the hand control sectors of the primary sensorimotor regions. The parietal region of interest, related to hand control (hereafter designated DPPChand), was defined permissively as the 95% confidence area of the parietal sites that were found to disrupt hand movements in the previously evoked per-operative study. In both hemispheres, irrespective of handedness, we found dense ipsilateral connections between a restricted part of DPPChand and focal sectors within the pre and postcentral gyrus. These sectors, corresponding to the hand territories of M1 and S1, targeted the same parietal zone (spatial overlap > 92%). As a sensitivity control, we searched for potential connections between the angular gyrus (AG) and the pre and postcentral regions. No robust pathways were found. Streamline densities identified using AG as the starting seed represented less than 5 % of the streamline densities identified from DPPChand. Together, these results support the existence of a direct sensory-parietal-motor loop suited for fast manual control and more generally, for any task requiring rapid integration of distal sensorimotor signals.
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