Concurrent neuroimaging and neurostimulation reveals a causal role for dlPFC in coding of task-relevant information.
17 May 2021-Vol. 4, Iss: 1, pp 588-588
TL;DR: In this article, the authors applied transcranial magnetic stimulation (TMS) during fMRI, and tested for causal changes in information coding, showing that TMS decreases coding of relevant information across frontoparietal cortex and the impact is significantly stronger than any effect on irrelevant information, which is not statistically detectable.
Abstract: Dorsolateral prefrontal cortex (dlPFC) is proposed to drive brain-wide focus by biasing processing in favour of task-relevant information. A longstanding debate concerns whether this is achieved through enhancing processing of relevant information and/or by inhibiting irrelevant information. To address this, we applied transcranial magnetic stimulation (TMS) during fMRI, and tested for causal changes in information coding. Participants attended to one feature, whilst ignoring another feature, of a visual object. If dlPFC is necessary for facilitation, disruptive TMS should decrease coding of attended features. Conversely, if dlPFC is crucial for inhibition, TMS should increase coding of ignored features. Here, we show that TMS decreases coding of relevant information across frontoparietal cortex, and the impact is significantly stronger than any effect on irrelevant information, which is not statistically detectable. This provides causal evidence for a specific role of dlPFC in enhancing task-relevant representations and demonstrates the cognitive-neural insights possible with concurrent TMS-fMRI-MVPA.
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TL;DR: In this paper , TMS single pulses were delivered to the left DLPFC at 100% motor threshold every 2.4s during TMS concurrent with n-back blocks.
5 citations
01 Jul 2022
TL;DR: In this paper , a review of previous concurrent TMS-functional magnetic resonance imaging (fMRI) studies that reported analyses of BOLD activity at the target location is presented. But, the authors conclude that the current evidence points to TMS inducing periods of increased and decreased neuronal firing that mostly cancel each other out and therefore lead to no change in the overall BOLD signal.
Abstract: Transcranial magnetic stimulation (TMS) is widely used for understanding brain function in neurologically intact subjects and for the treatment of various disorders. However, the precise neurophysiological effects of TMS at the site of stimulation remain poorly understood. The local effects of TMS can be studied using concurrent TMS-functional magnetic resonance imaging (fMRI), a technique where TMS is delivered during fMRI scanning. However, although concurrent TMS-fMRI was developed over 20 years ago and dozens of studies have used this technique, there is still no consensus on whether TMS increases blood oxygen level-dependent (BOLD) activity at the site of stimulation. To address this question, here we review all previous concurrent TMS-fMRI studies that reported analyses of BOLD activity at the target location. We find evidence that TMS increases local BOLD activity when stimulating the primary motor (M1) and visual (V1) cortices but that these effects are likely driven by the downstream consequences of TMS (finger twitches and phosphenes). However, TMS does not appear to increase BOLD activity at the site of stimulation for areas outside of the M1 and V1 when conducted at rest. We examine the possible reasons for such lack of BOLD signal increase based on recent work in nonhuman animals. We argue that the current evidence points to TMS inducing periods of increased and decreased neuronal firing that mostly cancel each other out and therefore lead to no change in the overall BOLD signal.
2 citations
TL;DR: The use of TMS to causally test dynamic causal modelings has been suggested previously (Hartwigsen et al., 2015; Best & Feredoes, 2013), and perturbation of a network with TMS has also contributed to the understanding of the relationship/dependence/separation between attention and WM as discussed by the authors .
Abstract: The study of human cognition through neuroscience has benefitted from a constant development and transformation of associated technology and analysis. Taking working memory (WM) as an example, and also as the area in which Mark Stokes has demonstrated this, it is a field that has often found itself right at the forefront of cognitive neuroscience, with a fascinating progression of theoretical insight and development as a consequence (see D’Esposito and Postle [2014] and Postle [2006] for thorough reviews of the field). Often, this innovation and discovery has produced theoretical upheaval, such as understanding the relationship/dependence/separation between attention and WM (Nobre & Stokes, 2011), challenging the limited capacity of WM (Bays & Husain, 2008), and the role of pFC in WM (Stokes, 2015) to name a few. (At this stage, the reader may have noted the involvement of Mark Stokes in several of these upheavals. It is not likely to be coincidence.) Throughout this journey over these last decades, TMS has been present, providing causal results to complement correlational neuroimaging, and behavioral studies. Despite its theoretical strengths as a class of research tools, the practical challenges and theoretical unknowns regarding physiological effects have meant that TMS has not always lived up to its promise as a “causal silver bullet” that can confirm correlational findings (Parkin, Ekhtiari, & Walsh, 2015; Siebner, Hartwigsen, Kassuba, & Rothwell, 2009). The future of TMS is, however, looking decidedly up (again). Advances in concurrent invasive recordings to visualize neuronal responses to TMS have provided invaluable in vivo evidence on the acute effects of stimulation (Allen, Pasley, Duong, & Freeman, 2007; Moliadze, Zhao, Eysel, & Funke, 2003). In humans, combining TMS with neuroimaging has also given the field a boost, showing effects across the brain (Bergmann et al., 2021), and on electrophysiological markers such as ERPs (Taylor, Walsh, & Eimer, 2008) and oscillations (Thut & Miniussi, 2009). Thus, as this commentary will argue, a crucial way forward for investigating cognition through neuroscience will be through innovative multimodal experimental approaches that can produce convergent evidence in one shot; examples will follow here as to how this approach can drive the field forward, with the focus being on TMS, given its superior spatial and temporal resolution compared with transcranial electrical stimulation. A game-changer for TMS has been in its simultaneous combination with fMRI and EEG. Although these innovations were demonstrated decades ago—for TMS-fMRI: Bohning et al. (1998); TMS-EEG: Ilmoniemi et al. (1997)— their progression from niche technical feats to essential contributors to cognitive neuroscience is coming to fruition. Given that one can now acquire reliable, wellsupported systems for TMS-fMRI and TMS-EEG, the next step is underway in which advanced experimental and analysis approaches will answer questions that would otherwise be difficult to address. The first growth area of cognitive TMS research is in the combination of TMS-fMRI with functional and effective connectivity analysis approaches; TMS can be applied to intervene with brain networks necessary for behavior and causally establish the necessity of associated network nodes and communication pathways. Although there exist many sophisticated methods for establishing connectivity to uncover brain networks through advanced mathematical and statistical algorithms, they all provide indirect “best current estimates” ( Johansen-Berg, 2013); what remains to be established is whether such connections exist beyond the statistical level and TMS is arguably the best placed to accomplish this. Nee and D’Esposito (2017) provided a valuable demonstration of what a TMS-fMRI-effective connectivity—here, dynamic causal modeling—approach can contribute theoretically. The use of TMS to causally test dynamic causal modelings has been suggested previously (Hartwigsen et al., 2015; Bestmann & Feredoes, 2013), and perturbation of a network with TMS has also been proposed in the context of network control theory (Medaglia, Pasqualetti, Hamilton, Thompson-Schill, & Bassett, 2017). A recent study by Sydnor et al. (2022) is a clear indication of the strengths of TMS-fMRI-perturbation, in which single pulses of TMS to ventrolateral pFC produced fMRI signal changes in the amygdala, the magnitude of which were predicted by the density of the white matter fiber pathway between the TMS target and amygdala. One could envisage a similar approach in which multiple lines of evidence including structure and function are combined to reveal patterns of connectivity for different types of behavioral tasks and/or different task stages. In the context of WM, such an approach may contribute to the ongoing debate University of Reading, United Kingdom
1 citations
TL;DR: In this article , the authors measured fMRI activity while participants performed a task designed to tag processing and control over feature-specific information that is task-relevant (targets) versus task-irrelevant (distractors).
Abstract: People can overcome a wide array of mental challenges by coordinating their neural information processing to align with their goals. Recent behavioral work has shown that people can independently control their attention across multiple features during perceptual decision-making, but the structure of the neural representations that enables this multivariate control remains mysterious. We hypothesized that the brain solves this complex coordination problem by orthogonalizing feature-specific representations of task demands and attentional priority, allowing the brain to independently monitor and adjust multiple streams of stimulus information. To test this hypothesis, we measured fMRI activity while participants performed a task designed to tag processing and control over feature-specific information that is task-relevant (targets) versus task-irrelevant (distractors). We then characterized the geometry of these neural representations using a novel multivariate analysis (Encoding Geometry Analysis), estimating where the encoding of different task features is correlated versus orthogonal. We identified feature-specific representations of task demands and attentional priority in the dorsal anterior cingulate cortex (dACC) and intraparietal sulcus (IPS), respectively, consistent with differential roles for these regions in monitoring versus directing information processing. Representations of attentional priority in IPS were fully mediated by the control requirements of the task, associated with behavioral performance, and depended on connectivity with nodes in the frontoparietal control network, suggesting that these representations serve a fundamental role in supporting attentional control. Together, these findings provide evidence for a neural geometry that can enable coordinated control over multiple sources of information.
1 citations
TL;DR: In this article, a high-speed, multimodal and synchronized system was presented to holistically examine neural processes that are involved in visually-guided reach-to-grasp planning and control.
Abstract: Background Previous brain-scanning research exploring the neural mechanisms underpinning visuomotor planning and control has mostly been done without simultaneous motion-tracking and eye-tracking. Employing concurrent methodologies would enhance understanding of the brain mechanisms underlying visuomotor integration of cognitive, visual, ocular, and motor aspects of reaching and grasping behaviours. Therefore, this work presents the methods and validation for a high-speed, multimodal and synchronized system to holistically examine neural processes that are involved in visually-guided movement. Methods The multimodal methods included high speed 3D motion tracking (Qualisys), 2D eye-tracking (SR Research), and magnetoencephalography (MEG; Elekta) that were synchronized to millisecond precision. Previous MRIs were taken to provide improved spatial localization. The methods section describes the system layout and acquisition parameters to achieve multimodal synchronization. Pilot results presented here are preliminary data from a larger study including 29 participants. Using a pincer grip, five people (3 male, 2 female, ages 30-32) reached for and grasped a translucent dowel 50 times, after it was pseudorandomly illuminated. The object illumination was the Go cue. Seven discrete time points (events) throughout the task were chosen for investigation of simultaneous brain, hand and eye activity associated with specific visual (Go cue), oculomotor (1st saccade after Go), motor (Reaction Time; RT, Maximum Velocity: MV, Maximum Grip Width; MGW) or cognitive (Ready, End) mechanisms. Time-frequency analyses were performed on the MEG data sourced from the left precentral gyrus to explore task-related changes time-locked to these chosen events. Pilot results Basic kinematic parameters including RT, MV, MGW, Movement Time, and Total Time were similar to previous, seminal research by Castiello, Paulignan and Jeannerod, (1991), using a similar task. Although no gaze instructions were given, eye-tracking results indicated volunteers mostly gazed at or near the target object when Ready (72%), and then hardly looked away throughout the rest of the task at the important events sampled here (92% - 98%). At the End event, when lifting the dowel, on average, participants gazed at or near the target object 100% of the time. Although saccades > 100 ms after Go, but prior to RT were made on average in about one fourth (M = 13, SD = 6) of trials, a mixed model (REML) indicated their latency in timing after the Go was significantly (F = 13.376, p = .001) associated with RT scores on those trials (AIC = 724, Rm2 = 0.407, Rc2= 0.420). Neural activity relative to baseline in the beta band was desynchronized for the visually guided reach periods, beginning prior to Go, and remaining sustained until beyond End, after the grasp and lift were executed. Conclusion This study presents the layout, acquisition parameters and validation for a multimodal, synchronized system designed to record data from the hand, eye and brain simultaneously, with millisecond precision during an ecologically-valid prehension task with physical, 3D objects. The pilot results align with previous research made with single or bimodal data recordings. This multimodal method enables full-brain modelling that can holistically map the precise location and timing of neural activity involved in the visual, oculomotor, motor and cognitive aspects of reach-to-grasp planning and control.
1 citations
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TL;DR: It is proposed that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them, which provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task.
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TL;DR: It is suggested that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain, featuring the presence of anticorrelated networks in the absence of overt task performance.
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