The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Evelina Fedorenko - One of the best experts on this subject based on the ideXlab platform.
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activity in the fronto parietal Multiple Demand network is robustly associated with individual differences in working memory and fluid intelligence
Cortex, 2020Co-Authors: Moataz Assem, Zachary Mineroff, Evelina Fedorenko, Idan Blank, Ahmet AdemogluAbstract:Abstract Numerous brain lesion and fMRI studies have linked individual differences in executive abilities and fluid intelligence to brain regions of the fronto-parietal “Multiple-Demand” (MD) network. Yet, fMRI studies have yielded conflicting evidence as to whether better executive abilities are associated with stronger or weaker MD activations and whether this relationship is restricted to the MD network. Here, in a large-sample (n = 216) fMRI investigation, we found that stronger activity in MD regions – functionally defined in individual participants – was robustly associated with more accurate and faster responses on a spatial working memory task performed in the scanner, as well as fluid intelligence measured independently (n = 114). In line with some prior claims about a relationship between language and fluid intelligence, we also found a weak association between activity in the brain regions of the left fronto-temporal language network during an independent passive reading task, and performance on the working memory task. However, controlling for the level of MD activity abolished this relationship, whereas the MD activity-behavior association remained highly reliable after controlling for the level of activity in the language network. Finally, we demonstrate how unreliable MD activity measures, coupled with small sample sizes, could falsely lead to the opposite, negative, association that has been reported in some prior studies. Taken together, these results demonstrate that a core component of individual differences variance in executive abilities and fluid intelligence is selectively and robustly positively associated with the level of activity in the MD network, a result that aligns well with lesion studies.
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the domain general Multiple Demand md network does not support core aspects of language comprehension a large scale fmri investigation
The Journal of Neuroscience, 2020Co-Authors: Evgeniia Diachek, Evelina Fedorenko, Idan Blank, Matthew Siegelman, Josef AffourtitAbstract:Aside from the language-selective left-lateralized frontotemporal network, language comprehension sometimes recruits a domain-general bilateral frontoparietal network implicated in executive functions: the Multiple Demand (MD) network. However, the nature of the MD network's contributions to language comprehension remains debated. To illuminate the role of this network in language processing in humans, we conducted a large-scale fMRI investigation using data from 30 diverse word and sentence comprehension experiments (481 unique participants [female and male], 678 scanning sessions). In line with prior findings, the MD network was active during many language tasks. Moreover, similar to the language-selective network, which is robustly lateralized to the left hemisphere, these responses were stronger in the left-hemisphere MD regions. However, in contrast with the language-selective network, the MD network responded more strongly (1) to lists of unconnected words than to sentences, and (2) in paradigms with an explicit task compared with passive comprehension paradigms. Indeed, many passive comprehension tasks failed to elicit a response above the fixation baseline in the MD network, in contrast to strong responses in the language-selective network. Together, these results argue against a role for the MD network in core aspects of sentence comprehension, such as inhibiting irrelevant meanings or parses, keeping intermediate representations active in working memory, or predicting upcoming words or structures. These results align with recent evidence of relatively poor tracking of the linguistic signal by the MD regions during naturalistic comprehension, and instead suggest that the MD network's engagement during language processing reflects effort associated with extraneous task Demands.SIGNIFICANCE STATEMENT Domain-general executive processes, such as working memory and cognitive control, have long been implicated in language comprehension, including in neuroimaging studies that have reported activation in domain-general Multiple Demand (MD) regions for linguistic manipulations. However, much prior evidence has come from paradigms where language interpretation is accompanied by extraneous tasks. Using a large fMRI dataset (30 experiments/481 participants/678 sessions), we demonstrate that MD regions are engaged during language comprehension in the presence of task Demands, but not during passive reading/listening, conditions that strongly activate the frontotemporal language network. These results present a fundamental challenge to proposals whereby linguistic computations, such as inhibiting irrelevant meanings, keeping representations active in working memory, or predicting upcoming elements, draw on domain-general executive resources.
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the domain general Multiple Demand md network does not support core aspects of language comprehension a large scale fmri investigation
bioRxiv, 2020Co-Authors: Evgeniia Diachek, Idan Blank, Matthew Siegelman, Josef Affourtit, Evelina FedorenkoAbstract:Aside from the language-selective left-lateralized fronto-temporal network, language comprehension sometimes additionally recruits a domain-general bilateral fronto-parietal network implicated in executive functions: the Multiple Demand (MD) network. However, the nature of the MD network9s contributions to language comprehension remains debated. To illuminate the role of this network in language processing, we conducted a large-scale fMRI investigation using data from 30 diverse word and sentence comprehension experiments (481 unique participants, 678 scanning sessions). In line with prior findings, the MD network was active during many language tasks. Moreover, similar to the language-selective network, which is robustly lateralized to the left hemisphere, these responses were stronger in the left-hemisphere MD regions. However, in stark contrast with the language-selective network, the MD network responded more strongly (i) to lists of unconnected words than to sentences, and critically, (ii) in paradigms with an explicit task compared to passive comprehension paradigms. In fact, many passive comprehension tasks failed to elicit a response above the fixation baseline in the MD network, in contrast to strong responses in the language-selective network. In tandem, these results argue against a role for the MD network in core aspects of sentence comprehension like inhibiting irrelevant meanings or parses, keeping intermediate representations active in working memory, or predicting upcoming words or structures. These results align with recent evidence of relatively poor tracking of the linguistic signal by the MD regions during naturalistic comprehension, and instead suggest that the MD network9s engagement during language processing likely reflects effort associated with extraneous task Demands.
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Broca's Area Is Not a Natural Kind.
Trends in cognitive sciences, 2020Co-Authors: Evelina Fedorenko, Idan BlankAbstract:Theories of human cognition prominently feature 'Broca's area', which causally contributes to a myriad of mental functions. However, Broca's area is not a monolithic, multipurpose unit - it is structurally and functionally heterogeneous. Some functions engaging (subsets of) this area share neurocognitive resources, whereas others rely on separable circuits. A decade of converging evidence has now illuminated a fundamental distinction between two subregions of Broca's area that likely play computationally distinct roles in cognition: one belongs to the domain-specific 'language network', the other to the domain-general 'Multiple-Demand (MD) network'. Claims about Broca's area should be (re)cast in terms of these (and other, as yet undetermined) functional components, to establish a cumulative research enterprise where empirical findings can be replicated and theoretical proposals can be meaningfully compared and falsified.
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fmri reveals language specific predictive coding during naturalistic sentence comprehension
Neuropsychologia, 2020Co-Authors: Cory Shain, Idan Blank, Marten Van Schijndel, William Schuler, Evelina FedorenkoAbstract:Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general Multiple-Demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.
Idan Blank - One of the best experts on this subject based on the ideXlab platform.
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incremental language comprehension difficulty predicts activity in the language network but not the Multiple Demand network
Cerebral Cortex, 2021Co-Authors: Leila Wehbe, Idan Blank, Cory Shain, Richard Futrell, Roger Levy, Titus Von Der MalsburgAbstract:What role do domain-general executive functions play in human language comprehension? To address this question, we examine the relationship between behavioral measures of comprehension and neural activity in the domain-general "Multiple Demand" (MD) network, which has been linked to constructs like attention, working memory, inhibitory control, and selection, and implicated in diverse goal-directed behaviors. Specifically, functional magnetic resonance imaging data collected during naturalistic story listening are compared with theory-neutral measures of online comprehension difficulty and incremental processing load (reading times and eye-fixation durations). Critically, to ensure that variance in these measures is driven by features of the linguistic stimulus rather than reflecting participant- or trial-level variability, the neuroimaging and behavioral datasets were collected in nonoverlapping samples. We find no behavioral-neural link in functionally localized MD regions; instead, this link is found in the domain-specific, fronto-temporal "core language network," in both left-hemispheric areas and their right hemispheric homotopic areas. These results argue against strong involvement of domain-general executive circuits in language comprehension.
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activity in the fronto parietal Multiple Demand network is robustly associated with individual differences in working memory and fluid intelligence
Cortex, 2020Co-Authors: Moataz Assem, Zachary Mineroff, Evelina Fedorenko, Idan Blank, Ahmet AdemogluAbstract:Abstract Numerous brain lesion and fMRI studies have linked individual differences in executive abilities and fluid intelligence to brain regions of the fronto-parietal “Multiple-Demand” (MD) network. Yet, fMRI studies have yielded conflicting evidence as to whether better executive abilities are associated with stronger or weaker MD activations and whether this relationship is restricted to the MD network. Here, in a large-sample (n = 216) fMRI investigation, we found that stronger activity in MD regions – functionally defined in individual participants – was robustly associated with more accurate and faster responses on a spatial working memory task performed in the scanner, as well as fluid intelligence measured independently (n = 114). In line with some prior claims about a relationship between language and fluid intelligence, we also found a weak association between activity in the brain regions of the left fronto-temporal language network during an independent passive reading task, and performance on the working memory task. However, controlling for the level of MD activity abolished this relationship, whereas the MD activity-behavior association remained highly reliable after controlling for the level of activity in the language network. Finally, we demonstrate how unreliable MD activity measures, coupled with small sample sizes, could falsely lead to the opposite, negative, association that has been reported in some prior studies. Taken together, these results demonstrate that a core component of individual differences variance in executive abilities and fluid intelligence is selectively and robustly positively associated with the level of activity in the MD network, a result that aligns well with lesion studies.
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the domain general Multiple Demand md network does not support core aspects of language comprehension a large scale fmri investigation
The Journal of Neuroscience, 2020Co-Authors: Evgeniia Diachek, Evelina Fedorenko, Idan Blank, Matthew Siegelman, Josef AffourtitAbstract:Aside from the language-selective left-lateralized frontotemporal network, language comprehension sometimes recruits a domain-general bilateral frontoparietal network implicated in executive functions: the Multiple Demand (MD) network. However, the nature of the MD network's contributions to language comprehension remains debated. To illuminate the role of this network in language processing in humans, we conducted a large-scale fMRI investigation using data from 30 diverse word and sentence comprehension experiments (481 unique participants [female and male], 678 scanning sessions). In line with prior findings, the MD network was active during many language tasks. Moreover, similar to the language-selective network, which is robustly lateralized to the left hemisphere, these responses were stronger in the left-hemisphere MD regions. However, in contrast with the language-selective network, the MD network responded more strongly (1) to lists of unconnected words than to sentences, and (2) in paradigms with an explicit task compared with passive comprehension paradigms. Indeed, many passive comprehension tasks failed to elicit a response above the fixation baseline in the MD network, in contrast to strong responses in the language-selective network. Together, these results argue against a role for the MD network in core aspects of sentence comprehension, such as inhibiting irrelevant meanings or parses, keeping intermediate representations active in working memory, or predicting upcoming words or structures. These results align with recent evidence of relatively poor tracking of the linguistic signal by the MD regions during naturalistic comprehension, and instead suggest that the MD network's engagement during language processing reflects effort associated with extraneous task Demands.SIGNIFICANCE STATEMENT Domain-general executive processes, such as working memory and cognitive control, have long been implicated in language comprehension, including in neuroimaging studies that have reported activation in domain-general Multiple Demand (MD) regions for linguistic manipulations. However, much prior evidence has come from paradigms where language interpretation is accompanied by extraneous tasks. Using a large fMRI dataset (30 experiments/481 participants/678 sessions), we demonstrate that MD regions are engaged during language comprehension in the presence of task Demands, but not during passive reading/listening, conditions that strongly activate the frontotemporal language network. These results present a fundamental challenge to proposals whereby linguistic computations, such as inhibiting irrelevant meanings, keeping representations active in working memory, or predicting upcoming elements, draw on domain-general executive resources.
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incremental language comprehension difficulty predicts activity in the language network but not the Multiple Demand network
bioRxiv, 2020Co-Authors: Leila Wehbe, Idan Blank, Cory Shain, Richard Futrell, Roger Levy, Titus Von Der MalsburgAbstract:Abstract What role do domain-general executive functions play in human language comprehension? To address this question, we examine the relationship between behavioral measures of comprehension and neural activity in the domain-general “Multiple Demand” (MD) network, which has been linked to constructs like attention, working memory, inhibitory control, and selection, and implicated in diverse goal-directed behaviors. Specifically, fMRI data collected during naturalistic story listening are compared to theory-neutral measures of online comprehension difficulty and incremental processing load (reading times and eye-fixation durations). Critically, to ensure that variance in these measures is driven by features of the linguistic stimulus rather than reflecting participant-or trial-level variability, the neuroimaging and behavioral datasets were collected in non-overlapping samples. We find no behavioral-neural link in functionally localized MD regions; instead, this link is found in the domain-specific, fronto-temporal “core language network”, in both left hemispheric areas and their right hemispheric homologues. These results argue against strong involvement of domain-general executive circuits in language comprehension.
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the domain general Multiple Demand md network does not support core aspects of language comprehension a large scale fmri investigation
bioRxiv, 2020Co-Authors: Evgeniia Diachek, Idan Blank, Matthew Siegelman, Josef Affourtit, Evelina FedorenkoAbstract:Aside from the language-selective left-lateralized fronto-temporal network, language comprehension sometimes additionally recruits a domain-general bilateral fronto-parietal network implicated in executive functions: the Multiple Demand (MD) network. However, the nature of the MD network9s contributions to language comprehension remains debated. To illuminate the role of this network in language processing, we conducted a large-scale fMRI investigation using data from 30 diverse word and sentence comprehension experiments (481 unique participants, 678 scanning sessions). In line with prior findings, the MD network was active during many language tasks. Moreover, similar to the language-selective network, which is robustly lateralized to the left hemisphere, these responses were stronger in the left-hemisphere MD regions. However, in stark contrast with the language-selective network, the MD network responded more strongly (i) to lists of unconnected words than to sentences, and critically, (ii) in paradigms with an explicit task compared to passive comprehension paradigms. In fact, many passive comprehension tasks failed to elicit a response above the fixation baseline in the MD network, in contrast to strong responses in the language-selective network. In tandem, these results argue against a role for the MD network in core aspects of sentence comprehension like inhibiting irrelevant meanings or parses, keeping intermediate representations active in working memory, or predicting upcoming words or structures. These results align with recent evidence of relatively poor tracking of the linguistic signal by the MD regions during naturalistic comprehension, and instead suggest that the MD network9s engagement during language processing likely reflects effort associated with extraneous task Demands.
John S Duncan - One of the best experts on this subject based on the ideXlab platform.
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a domain general cognitive core defined in multimodally parcellated human cortex
Cerebral Cortex, 2020Co-Authors: Moataz Assem, John S Duncan, Matthew F Glasser, David C Van EssenAbstract:Numerous brain imaging studies identified a domain-general or "Multiple-Demand" (MD) activation pattern accompanying many tasks and may play a core role in cognitive control. Though this finding is well established, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding the precise anatomy, functional differentiation, and connectivity of the MD system. To address these limitations, we used data from 449 subjects from the Human Connectome Project, with the cortex of each individual parcellated using neurobiologically grounded multimodal MRI features. The conjunction of three cognitive contrasts reveals a core of 10 widely distributed MD parcels per hemisphere that are most strongly activated and functionally interconnected, surrounded by a penumbra of 17 additional areas. Outside cerebral cortex, MD activation is most prominent in the caudate and cerebellum. Comparison with canonical resting-state networks shows MD regions concentrated in the fronto-parietal network but also engaging three other networks. MD activations show modest relative task preferences accompanying strong co-recruitment. With distributed anatomical organization, mosaic functional preferences, and strong interconnectivity, we suggest MD regions are well positioned to integrate and assemble the diverse components of cognitive operations. Our precise delineation of MD regions provides a basis for refined analyses of their functions.
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a domain general cognitive core defined in multimodally parcellated human cortex
bioRxiv, 2019Co-Authors: Moataz Assem, John S Duncan, Matthew F Glasser, David C Van EssenAbstract:Abstract Numerous brain imaging studies identified a domain-general or “Multiple-Demand” (MD) activation pattern accompanying many tasks and may play a core role in cognitive control. Though this finding is well established, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding the precise anatomy, functional differentiation and connectivity of the MD system. To address these limitations, we used data from 449 subjects from the Human Connectome Project, with cortex of each individual parcellated using neurobiologically grounded multi-modal MRI features. The conjunction of three cognitive contrasts reveals a core of 10 widely distributed MD parcels per hemisphere that are most strongly activated and functionally interconnected, surrounded by a penumbra of 17 additional areas. Outside cerebral cortex, MD activation is most prominent in the caudate and cerebellum. Comparison with canonical resting state networks shows MD regions concentrated in the fronto-parietal network but also engaging three other networks. MD activations show modest relative task preferences accompanying strong co-recruitment. With distributed anatomical organization, mosaic functional preferences, and strong interconnectivity, we suggest MD regions are well positioned to integrate and assemble the diverse components of cognitive operations. Our precise delineation of MD regions provides a basis for refined analyses of their functions.
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Response of the Multiple-Demand network during simple stimulus discriminations
2018Co-Authors: Tanya Wen, Daniel J Mitchell, John S DuncanAbstract:The Multiple-Demand (MD) network is sensitive to many aspects of task difficulty, including such factors as rule complexity, memory load, attentional switching and inhibition. Many accounts link MD activity to top-down task control, raising the question of response when performance is limited by the quality of sensory input, and indeed, some prior results suggest little effect of sensory manipulations. Here we examined judgments of motion direction, manipulating difficulty by either motion coherence or salience of irrelevant dots. We manipulated each difficulty type across six levels, from very easy to very hard, and additionally manipulated whether difficulty level was blocked, and thus known in advance, or randomized. Despite the very large manipulations employed, difficulty had little effect on MD activity, especially for the coherence manipulation. Contrasting with these small or absent effects, we observed the usual increase of MD activity with increased rule complexity. We suggest that, for simple sensory discriminations, it may be impossible to compensate for reduced stimulus information by increased top-down control.
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review coding of visual auditory rule and response information in the brain 10 years of multivoxel pattern analysis
Journal of Cognitive Neuroscience, 2016Co-Authors: Alexandra Woolgar, John S Duncan, Jade JacksonAbstract:How is the processing of task information organized in the brain? Many views of brain function emphasize modularity, with different regions specialized for processing different types of information. However, recent accounts also highlight flexibility, pointing especially to the highly consistent pattern of frontoparietal activation across many tasks. Although early insights from functional imaging were based on overall activation levels during different cognitive operations, in the last decade many researchers have used multivoxel pattern analyses to interrogate the representational content of activations, mapping out the brain regions that make particular stimulus, rule, or response distinctions. Here, we drew on 100 searchlight decoding analyses from 57 published papers to characterize the information coded in different brain networks. The outcome was highly structured. Visual, auditory, and motor networks predominantly but not exclusively coded visual, auditory, and motor information, respectively. By contrast, the frontoparietal Multiple-Demand network was characterized by domain generality, coding visual, auditory, motor, and rule information. The contribution of the default mode network and voxels elsewhere was minor. The data suggest a balanced picture of brain organization in which sensory and motor networks are relatively specialized for information in their own domain, whereas a specific frontoparietal network acts as a domain-general "core" with the capacity to code many different aspects of a task.
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neural coding for instruction based task sets in human frontoparietal and visual cortex
Cerebral Cortex, 2016Co-Authors: Daniel J Mitchell, Paul S Muhlekarbe, John S Duncan, Wouter De Baene, Marcel BrassAbstract:Task preparation has traditionally been thought to rely upon persistent representations of instructions that permit their execution after delays. Accumulating evidence suggests, however, that accurate retention of task knowledge can be insufficient for successful performance. Here, we hypothesized that instructed facts would be organized into a task set; a temporary coding scheme that proactively tunes sensorimotor pathways according to instructions to enable highly efficient "reflex-like" performance. We devised a paradigm requiring either implementation or memorization of novel stimulus-response mapping instructions, and used multivoxel pattern analysis of neuroimaging data to compare neural coding of instructions during the pretarget phase. Although participants could retain instructions under both Demands, we observed striking differences in their representation. To-be-memorized instructions could only be decoded from mid-occipital and posterior parietal cortices, consistent with previous work on visual short-term memory storage. In contrast, to-be-implemented instructions could also be decoded from frontoparietal "Multiple-Demand" regions, and dedicated visual areas, implicated in processing instructed stimuli. Neural specificity in the latter moreover correlated with performance speed only when instructions were prepared, likely reflecting the preconfiguration of instructed decision circuits. Together, these data illuminate how the brain proactively optimizes performance, and help dissociate neural mechanisms supporting task control and short-term memory storage.
John Duncan - One of the best experts on this subject based on the ideXlab platform.
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roles of the default mode and Multiple Demand networks in naturalistic versus symbolic decisions
The Journal of Neuroscience, 2021Co-Authors: Verity Smith, John Duncan, Daniel J MitchellAbstract:The default mode network (DMN) is often associated with representing semantic, social and situational content of contexts and episodes. The DMN may therefore be important for contextual decision-making, through representing situational constraints and simulating common courses of events. Most decision-making paradigms, however, use symbolic stimuli and instead implicate cognitive control regions such as the Multiple Demand (MD) system. This fMRI study aimed to contrast the brain mechanisms underlying decision-making based on rich naturalistic contexts or symbolic cues. Whilst performing an ongoing task, 40 human participants (25 female) responded to different sounds. For one sound, the stimulus-response mapping was fixed; responses for the other sounds depended on the visual context: either lifelike scenes or letter symbols, varying across participants. Despite minimal behavioural differences between the groups, posterior DMN regions showed increased activity during context-dependent decision-making using the naturalistic scenes only, compared to symbolic cues. More anterior temporal and frontal DMN regions showed a different pattern, with sensitivity to the need for contextual control, but not to the type of context. Furthermore, in the scenes group, widespread DMN regions showed stronger representation of not just the context but also the sound whose significance it modulated. In comparison, the MD system showed strong univariate activity for every decision, but, intriguingly, somewhat reduced activity in the case of a scene-based but Demanding context-dependent decisions. Depending on context, we suggest, either DMN or MD regions may play a prominent role in selection and control of appropriate behaviour. SIGNIFICANCE STATEMENT: Contextual knowledge is widely believed to be important for guiding real-world goal-directed behaviour. Much remains to be understood, however, regarding the underlying brain mechanisms. Using a novel paradigm to contrast decisions based on richly meaningful naturalistic scenes with decisions based on symbolic cues, we find that both Multiple Demand regions and default mode regions may contribute to the cognitive control of behaviour. Rich semantic context enhances representation not just of the context itself, but also of the contents of the decision that it controls. Dependence of a decision on naturalistic context can also reverse the common pattern of Multiple Demand regions responding more, and default mode regions responding less, to more difficult decisions.
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hierarchical representation of multistep tasks in Multiple Demand and default mode networks
The Journal of Neuroscience, 2020Co-Authors: Tanya Wen, John Duncan, Daniel J MitchellAbstract:Task episodes consist of sequences of steps that are performed to achieve a goal. We used fMRI to examine neural representation of task identity, component items, and sequential position, focusing on two major cortical systems – the Multiple-Demand (MD) and default mode networks (DMN). Human participants (20 male, 22 female) learned six tasks each consisting of four steps. Inside the scanner, participants were cued which task to perform and then sequentially identified the target item of each step in the correct order. Univariate time-course analyses indicated that intra-episode progress was tracked by a tonically increasing global response, plus an increasing phasic step response specific to MD regions. Inter-episode boundaries evoked a widespread response at episode onset, plus a marked offset response specific to DMN regions. Representational similarity analysis was used to examine representation of task identity and component steps. Both networks represented the content and position of individual steps, however the DMN preferentially represented task identity while the MD network preferentially represented step-level information. Thus, although both MD and DMN networks are sensitive to step-level and episode-level information in the context of hierarchical task performance, they exhibit dissociable profiles in terms of both temporal dynamics and representational content. The results suggest collaboration of Multiple brain regions in control of multi-step behavior, with MD regions particularly involved in processing the detail of individual steps, and DMN adding representation of broad task context. SIGNIFICANCE STATEMENT Achieving one’s goals requires knowing what to do and when. Tasks are typically hierarchical, with smaller steps nested within overarching goals. For effective, flexible behavior, the brain must represent both levels. We contrast response time-courses and information content of two major cortical systems – the Multiple-Demand (MD) and default mode networks (DMN) – during multi-step task episodes. Both networks are sensitive to step-level and episode-level information, but with dissociable profiles. Intra-episode progress is tracked by tonically increasing global responses, plus MD-specific increasing phasic step responses. Inter-episode boundaries evoke widespread responses at episode onset, plus DMN-specific offset responses. Both networks represent content and position of individual steps; however, the DMN and MD networks favor task identity and step-level information respectively.
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hierarchical representation of multi step tasks in Multiple Demand and default mode networks
bioRxiv, 2020Co-Authors: Tanya Wen, John Duncan, Daniel J MitchellAbstract:Task episodes consist of sequences of steps that are performed to achieve a goal. The current study used fMRI to examine which regions of the brain represent full episodes, component items, and sequential position. Participants learned six tasks each consisting of four steps. Inside the scanner, participants were cued which task to perform and then sequentially identified the target item of each step in the correct order. The Multiple Demand (MD) network and the visual cortex exhibited phasic responses to each task step, suggesting sensitivity to the fine structure of the episode. In addition, many brain regions - including regions of the default mode network (DMN) - showed a phasic response to onset and offset of the entire task episode, along with gradually increasing activity across the episode. Representational similarity analysis was used to examine encoding of component items and entire episodes. Compared to MD regions, which coded individual items but not the entire episode, the DMN showed representation of both item and episode. The results suggest collaboration of Multiple brain regions in control of multi-step behavior, with MD regions representing the detail of individual steps, and DMN adding representation of broad task context.
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progressive recruitment of the frontoparietal Multiple Demand system with increased task complexity time pressure and reward
Journal of Cognitive Neuroscience, 2019Co-Authors: Sneha Shashidhara, Daniel J Mitchell, Yaara Erez, John DuncanAbstract:A distributed, frontoparietal “Multiple-Demand” (MD) network is involved in tasks of many different kinds. Integrated activity across this network may be needed to bind together the Multiple featur...
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the Multiple Demand system but not the language system supports fluid intelligence
Nature Human Behaviour, 2018Co-Authors: Alexandra Woolgar, Facundo Manes, John Duncan, Evelina FedorenkoAbstract:A set of frontoparietal brain regions - the Multiple-Demand (MD) system [1, 2] - has been linked to fluid intelligence in brain imaging [3, 4] and in studies of patients with brain damage [5-7].For example, the amount of damage to frontal or parietal, but not temporal, cortices predicts fluid intelligence deficit [5]. However, frontal and parietal lobes are structurally [8] and functionally [9, 10] heterogeneous. They contain domain-general regions that respond across diverse tasks [11, 12], but also specialized regions that respond selectively during language processing [13]. Since language may be critical for complex thought [14-24, cf. 25-26], intelligence loss following damage to frontoparietal cortex could have important contributions from damage to language-selective regions. To evaluate the relative contributions of MD vs. language-selective regions, we employed large fMRI datasets to construct probabilistic maps of the two systems. We used these maps to weigh the volume of lesion (in each of 80 patients) falling within each system. MD-weighted, but not language-weighted, lesion volumes predicted fluid intelligence deficit (with the opposite pattern observed for verbal fluency), suggesting that fluid intelligence is specifically tied to the MD system, and undermining claims that language is at the core of complex thought.
Nancy Kanwisher - One of the best experts on this subject based on the ideXlab platform.
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reduced language lateralization in autism and the broader autism phenotype as assessed with robust individual subjects analyses
bioRxiv, 2020Co-Authors: Olessia Jouravlev, Zachary Mineroff, Amanda J Haskins, Dima Ayyash, Alexander J E Kell, Nancy Kanwisher, Evelina FedorenkoAbstract:One of the few replicated functional brain differences between individuals with autism spectrum disorders (ASD) and neurotypical (NT) controls is reduced language lateralization. However, most prior reports relied on comparisons of group-level activation maps or functional markers that had not been validated at the individual-subject level, and/or used tasks that do not isolate language processing from other cognitive processes, complicating interpretation. Furthermore, few prior studies have examined functional responses in other functional networks, as needed to determine the selectivity of the effect. Using fMRI, we compared language lateralization between 28 ASD participants and carefully pairwise-matched controls, with the language regions defined individually with a well-validated language localizer. ASD participants showed less lateralized responses due to stronger right hemisphere activations. Further, this effect did not stem from a ubiquitous reduction in lateralization across the brain: ASD participants did not differ from controls in the lateralization of two other large-scale networks - the Theory of Mind network and the Multiple Demand network. Finally, in an exploratory study, we tested whether reduced language lateralization may also be present in NT individuals with high autistic trait load. Indeed, autistic trait load in a large set of NT participants (n=189) was associated with less lateralized language activations. These results suggest that reduced language lateralization is a robust and spatially selective neural marker of autism, present in individuals with ASD, but also in NT individuals with higher genetic liability for ASD, in line with a continuum model of underlying genetic risk.
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functional neuroanatomy of intuitive physical inference
Proceedings of the National Academy of Sciences of the United States of America, 2016Co-Authors: Jason Fischer, John G Mikhael, Joshua B Tenenbaum, Nancy KanwisherAbstract:To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "Multiple Demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.
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a functional dissociation between language and Multiple Demand systems revealed in patterns of bold signal fluctuations
Journal of Neurophysiology, 2014Co-Authors: Idan Blank, Nancy Kanwisher, Evelina FedorenkoAbstract:What is the relationship between language and other high-level cognitive functions? Neuroimaging studies have begun to illuminate this question, revealing that some brain regions are quite selectively engaged during language processing, whereas other “Multiple-Demand” (MD) regions are broadly engaged by diverse cognitive tasks. Nonetheless, the functional dissociation between the language and MD systems remains controversial. Here, we tackle this question with a synergistic combination of functional MRI methods: we first define candidate language-specific and MD regions in each subject individually (using functional localizers) and then measure blood oxygen level-dependent signal fluctuations in these regions during two naturalistic conditions (“rest” and story-comprehension). In both conditions, signal fluctuations strongly correlate among language regions as well as among MD regions, but correlations across systems are weak or negative. Moreover, data-driven clustering analyses based on these inter-region correlations consistently recover two clusters corresponding to the language and MD systems. Thus although each system forms an internally integrated whole, the two systems dissociate sharply from each other. This independent recruitment of the language and MD systems during cognitive processing is consistent with the hypothesis that these two systems support distinct cognitive functions.
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a functional dissociation between language and Multiple Demand systems revealed in patterns of bold signal fluctuations
Journal of Neurophysiology, 2014Co-Authors: Idan Blank, Nancy Kanwisher, Evelina FedorenkoAbstract:What is the relationship between language and other high-level cognitive functions? Neuroimaging studies have begun to illuminate this question, revealing that some brain regions are quite selectiv...
