Πέμπτη, 14 Μαρτίου 2019

NeuroImage

Benchmarking functional connectome-based predictive models for resting-state fMRI

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Kamalaker Dadi, Mehdi Rahim, Alexandre Abraham, Darya Chyzhyk, Michael Milham, Bertrand Thirion, Gaël Varoquaux, for the Alzheimer's Disease Neuroimaging Initiative

Abstract

Functional connectomes reveal biomarkers of individual psychological or clinical traits. However, there is great variability in the analytic pipelines typically used to derive them from rest-fMRI cohorts. Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the best modeling choices. We systematically study the prediction performances of models in 6 different cohorts and a total of 2000 individuals, encompassing neuro-degenerative (Alzheimer's, Post-traumatic stress disorder), neuro-psychiatric (Schizophrenia, Autism), drug impact (Cannabis use) clinical settings and psychological trait (fluid intelligence). The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning. For each step we benchmark typical choices: 8 different ways of defining regions –either pre-defined or generated from the rest-fMRI data– 3 measures to build functional connectomes from the extracted time-series, and 10 classification models to compare functional interactions across subjects. Our benchmarks summarize more than 240 different pipelines and outline modeling choices that show consistent prediction performances in spite of variations in the populations and sites. We find that regions defined from functional data work best; that it is beneficial to capture between-region interactions with tangent-based parametrization of covariances, a midway between correlations and partial correlation; and that simple linear predictors such as a logistic regression give the best predictions. Our work is a step forward to establishing reproducible imaging-based biomarkers for clinical settings.

Graphical abstract

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Frequency and power of human alpha oscillations drift systematically with time-on-task

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Christopher S.Y. Benwell, Raquel E. London, Chiara F. Tagliabue, Domenica Veniero, Joachim Gross, Christian Keitel, Gregor Thut

Abstract

Oscillatory neural activity is a fundamental characteristic of the mammalian brain spanning multiple levels of spatial and temporal scale. Current theories of neural oscillations and analysis techniques employed to investigate their functional significance are based on an often implicit assumption: In the absence of experimental manipulation, the spectral content of any given EEG- or MEG-recorded neural oscillator remains approximately stationary over the course of a typical experimental session (∼1 h), spontaneously fluctuating only around its dominant frequency. Here, we examined this assumption for ongoing neural oscillations in the alpha-band (8–13 Hz). We found that alpha peak frequency systematically decreased over time, while alpha-power increased. Intriguingly, these systematic changes showed partial independence of each other: Statistical source separation (independent component analysis) revealed that while some alpha components displayed concomitant power increases and peak frequency decreases, other components showed either unique power increases or frequency decreases. Interestingly, we also found these components to differ in frequency. Components that showed mixed frequency/power changes oscillated primarily in the lower alpha-band (∼8–10 Hz), while components with unique changes oscillated primarily in the higher alpha-band (∼9–13 Hz). Our findings provide novel clues on the time-varying intrinsic properties of large-scale neural networks as measured by M/EEG, with implications for the analysis and interpretation of studies that aim at identifying functionally relevant oscillatory networks or at driving them through external stimulation.



Speech processing and plasticity in the right hemisphere predict variation in adult foreign language learning

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Zhenghan Qi, Michelle Han, Yunxin Wang, Carlo de los Angeles, Qi Liu, Keri Garel, Ee San Chen, Susan Whitfield-Gabrieli, John D.E. Gabrieli, Tyler K. Perrachione

Abstract

Foreign language learning in adulthood often takes place in classrooms where learning outcomes vary widely among students, for both initial learning and long-term retention. Despite the fundamental role of speech perception in first language acquisition, its role in foreign language learning outcomes remains unknown. Using a speech discrimination functional magnetic resonance imaging (fMRI) task and resting-state fMRI before and after an intensive, classroom-based, Mandarin Chinese course, we examined how variations in pre-training organization and pre-to-post reorganization of brain functions predicted successful language learning in male and female native English-speakers. Greater pre-training activation in right inferior frontal gyrus (IFG) to Mandarin speech was associated with better Mandarin attainment at the end of the course. After four weeks of class, learners showed overall increased activation in left IFG and left superior parietal lobule (SPL) to Mandarin speech, but in neither region was variation related to learning outcomes. Immediate attainment was associated with greater pre-to-post reduction of right IFG activation to Mandarin speech but also greater enhancement of resting-state connectivity between this region and both left IFG and left SPL. Long-term retention of Mandarin skills measured three months later was more accurately predicted by models using features of neural preparedness (pre-training activation) and neural plasticity (pre-to-post activation change) than models using behavior preparedness and plasticity features (pre-training speech discrimination accuracy and Mandarin attainment, respectively). These findings suggest that successful holistic foreign language acquisition in human adulthood requires right IFG engagement during initial learning but right IFG disengagement for long-term retention of language skills.



Auditory and language contributions to neural encoding of speech features in noisy environments

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Jiajie Zou, Jun Feng, Tianyong Xu, Peiqing Jin, Cheng Luo, Jianfeng Zhang, Xunyi Pan, Feiyan Chen, Jing Zheng, Nai Ding

Abstract

Recognizing speech in noisy environments is a challenging task that involves both auditory and language mechanisms. Previous studies have demonstrated human auditory cortex can reliably track the temporal envelope of speech in noisy environments, which provides a plausible neural basis for noise-robust speech recognition. The current study aimed at teasing apart auditory and language contributions to noise-robust envelope tracking by comparing the neural responses of 2 groups of listeners, i.e., native listeners and foreign listeners who did not understand the testing language. In the experiment, speech signals were mixed with spectrally matched stationary noise at 4 intensity levels and listeners' neural responses were recorded using electroencephalography (EEG). When the noise intensity increased, the neural response gain increased in both groups of listeners, demonstrating auditory gain control. Language comprehension generally reduced the response gain and envelope-tracking precision, and modulated the spatial and temporal profile of envelope-tracking activity. Based on the spatio-temporal dynamics of envelope-tracking activity, a linear classifier can jointly decode the 2 listener groups and 4 levels of noise intensity. Altogether, the results showed that without feedback from language processing, auditory mechanisms such as gain control can lead to a noise-robust speech representation. High-level language processing modulated the spatio-temporal profile of the neural representation of speech envelope, instead of generally enhancing the envelope representation.



Brain regions preferentially responding to transient and iso-intense painful or tactile stimuli

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Q. Su, W. Qin, Q.Q. Yang, C.S. Yu, T.Y. Qian, A. Mouraux, G.D. Iannetti, M. Liang

Abstract

How pain emerges from cortical activities remains an unresolved question in pain neuroscience. A first step toward addressing this question consists in identifying brain activities that occur preferentially in response to painful stimuli in comparison to non-painful stimuli. A key confound that has affected this important comparison in many previous studies is the intensity of the stimuli generating painful and non-painful sensations. Here, we compared the brain activity during iso-intense painful and tactile sensations sampled by functional MRI in 51 healthy participants. Specifically, the perceived intensity was recorded for every stimulus and only the stimuli with rigorously matched perceived intensity were selected and compared between painful and tactile conditions. We found that all brain areas activated by painful stimuli were also activated by tactile stimuli, and vice versa. Neural responses in these areas were correlated with the perceived stimulus intensity, regardless of stimulus modality. More importantly, among these activated areas, we further identified a number of brain regions showing stronger responses to painful stimuli than to tactile stimuli when perceived intensity was carefully matched, including the bilateral opercular cortex, the left supplementary motor area and the right frontal middle and inferior areas. Among these areas, the right frontal middle area still responded more strongly to painful stimuli even when painful stimuli were perceived less intense than tactile stimuli, whereas in this condition other regions showed stronger responses to tactile stimuli. In contrast, the left postcentral gyrus, the visual cortex, the right parietal inferior gyrus, the left parietal superior gyrus and the right cerebellum had stronger responses to tactile stimuli than to painful stimuli when perceived intensity was matched. When tactile stimuli were perceived less intense than painful stimuli, the left postcentral gyrus and the right parietal inferior gyrus still responded more strongly to tactile stimuli while other regions now showed similar responses to painful and tactile stimuli. These results suggest that different brain areas may be engaged differentially when processing painful and tactile information, although their neural activities are not exclusively dedicated to encoding information of only one modality but are strongly determined by perceived stimulus intensity regardless of stimulus modality.



Differences in functional connectivity along the anterior-posterior axis of human hippocampal subfields

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Marshall A. Dalton, Cornelia McCormick, Eleanor A. Maguire

Abstract

There is a paucity of information about how human hippocampal subfields are functionally connected to each other and to neighbouring extra-hippocampal cortices. In particular, little is known about whether patterns of functional connectivity (FC) differ down the anterior-posterior axis of each subfield. Here, using high resolution structural MRI we delineated the hippocampal subfields in healthy young adults. This included the CA fields, separating DG/CA4 from CA3, separating the pre/parasubiculum from the subiculum, and also segmenting the uncus. We then used high resolution resting state functional MRI to interrogate FC. We first analysed the FC of each hippocampal subfield in its entirety, in terms of FC with other subfields and with the neighbouring regions, namely entorhinal, perirhinal, posterior parahippocampal and retrosplenial cortices. Next, we analysed FC for different portions of each hippocampal subfield along its anterior-posterior axis, in terms of FC between different parts of a subfield, FC with other subfield portions, and FC of each subfield portion with the neighbouring cortical regions of interest. We found that intrinsic functional connectivity between the subfields aligned generally with the tri-synaptic circuit but also extended beyond it. Our findings also revealed that patterns of functional connectivity between the subfields and neighbouring cortical areas differed markedly along the anterior-posterior axis of each hippocampal subfield. Overall, these results contribute to ongoing efforts to characterise human hippocampal subfield connectivity, with implications for understanding hippocampal function.



A dual architecture for the cognitive control of language: Evidence from functional imaging and language production

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Nicolas J. Bourguignon, Vincent L. Gracco

Abstract

The relation between language processing and the cognitive control of thought and action is a widely debated issue in cognitive neuroscience. While recent research suggests a modular separation between a 'language system' for meaningful linguistic processing and a 'multiple-demand system' for cognitive control, other findings point to more integrated perspectives in which controlled language processing emerges from a division of labor between (parts of) the language system and (parts of) the multiple-demand system. We test here a dual approach to the cognitive control of language predicated on the notion of cognitive control as the combined contribution of a semantic control network (SCN) and a working memory network (WMN) supporting top-down manipulation of (lexico-)semantic information and the monitoring of information in verbal working memory, respectively. We reveal these networks in a large-scale coordinate-based meta-analysis contrasting functional imaging studies of verbal working memory vs. active judgments on (lexico-)semantic information and show the extent of their overlap with the multiple-demand system and the language system. Testing these networks' involvement in a functional imaging study of object naming and verb generation, we then show that SCN specializes in top-down retrieval and selection of (lexico-)semantic representations amongst competing alternatives, while WMN intervenes at a more general level of control modulated in part by the amount of competing responses available for selection. These results have implications in conceptualizing the neurocognitive architecture of language and cognitive control.



The disentanglement of the neural and experiential complexity of self-generated thoughts: A users guide to combining experience sampling with neuroimaging data

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Léa M. Martinon, Jonathan Smallwood, Deborah McGann, Colin Hamilton, Leigh M. Riby

Abstract

Human cognition is not limited to the processing of events in the external environment, and the covert nature of certain aspects of the stream of consciousness (e.g. experiences such as mind-wandering) provides a methodological challenge. Although research has shown that we spend a substantial amount of time focused on thoughts and feelings that are intrinsically generated, evaluating such internal states, purely on psychological grounds can be restrictive. In this review of the different methods used to examine patterns of ongoing thought, we emphasise how the process of triangulation between neuroimaging techniques, with self-reported information, is important for the development of a more empirically grounded account of ongoing thought. Specifically, we show how imaging techniques have provided critical information regarding the presence of covert states and can help in the attempt to identify different aspects of experience.



Neural variability quenching during decision-making: Neural individuality and its prestimulus complexity

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Annemarie Wolff, Lin Yao, Javier Gomez-Pilar, Mahsa Shoaran, Ning Jiang, Georg Northoff

Abstract

The spontaneous activity of the brain interacts with stimulus-induced activity which is manifested in event-related amplitude and its trial-to-trial variability (TTV). TTV describes the variability in the amplitude of the stimulus-evoked response across trials, and it is generally observed to be reduced, or quenched. While such TTV quenching has been observed on both the cellular and regional levels, its exact behavioral relevance and neuronal basis remains unclear. Applying a novel paradigm for testing neural markers of individuality in internally-guided decision-making, we here investigated whether TTV (i) represents an individually specific response by comparing individualized vs shared stimuli; and (ii) is mediated by the complexity of prestimulus activity as measured by the Lempel-Ziv Complexity index (LZC). We observed that TTV - and other electrophysiological markers such as ERP, ERSP, and ITC – showed first significant differences between individualized and shared stimuli (while controlling for task-related effects) specifically in the alpha and beta frequency bands, and secondly that TTV in the beta band correlated significantly with reaction time and eLORETA activity. Moreover, we demonstrate that the complexity (LZC) of neuronal activity is higher in the prestimulus period while it decreases during the poststimulus period, with the former also correlating specifically with poststimulus individualized TTV in alpha (but not with shared TTV). Together, our results show that the TTV represents a marker of 'neural individualization' which, being related to internal processes on both neural and psychological levels, is mediated by the information complexity of prestimulus activity. More generally, our results inform the pre-post-stimulus dynamics of rest-stimulus interaction, which is a basic and ubiquitous neural phenomenon in the brain and highly relevant for mental features including their individuality.



Probing cortical and sub-cortical contributions to instruction-based learning: Regional specialisation and global network dynamics

Publication date: 15 May 2019

Source: NeuroImage, Volume 192

Author(s): Adam Hampshire, Richard E. Daws, Ines Das Neves, Eyal Soreq, Stefano Sandrone, Ines R. Violante

Abstract

Diverse cortical networks and striatal brain regions are implicated in instruction-based learning (IBL); however, their distinct contributions remain unclear. We use a modified fMRI paradigm to test two hypotheses regarding the brain mechanisms that underlie IBL. One hypothesis proposes that anterior caudate and frontoparietal regions transiently co-activate when new rules are being bound in working memory. The other proposes that they mediate the application of the rules at different stages of the consolidation process. In accordance with the former hypothesis, we report strong activation peaks within and increased connectivity between anterior caudate and frontoparietal regions when rule-instruction slides are presented. However, similar effects occur throughout a broader set of cortical and sub-cortical regions, indicating a metabolically costly reconfiguration of the global brain state. The distinct functional roles of cingulo-opercular, frontoparietal and default-mode networks are apparent from their activation throughout, early and late in the practice phase respectively. Furthermore, there is tentative evidence of a peak in anterior caudate activity mid-way through the practice stage. These results demonstrate how performance of the same simple task involves a steadily shifting balance of brain systems as learning progresses. They also highlight the importance of distinguishing between regional specialisation and global dynamics when studying the network mechanisms that underlie cognition and learning.



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