Cognitive Neuroscience Applications Research Articles

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods’ efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

Feasibility of high-resolution perfusion imaging using arterial spin labeling MRI at 3 Tesla.

Cerebral blood flow (CBF) is a critical physiological parameter of brain health, and it can be non-invasively measured with arterial spin labeling (ASL) MRI. In this study, we evaluated and optimized whole-brain, high-resolution ASL as an alternative to the low-resolution ASL employed in the routine assessment of CBF in both healthy participants and patients. Two high-resolution protocols (i.e., pCASL and FAIR-Q2TIPS (PASL) with 2 mm isotropic voxels) were compared to a default clinical pCASL protocol (3.4 × 3.4 × 4 mm 3), all of whom had an acquisition time of ≈ 5 min. We assessed the impact of high-resolution acquisition on reducing partial voluming and improving sensitivity to the perfusion signal, and evaluated the effectiveness of z-deblurring on the ASL data. We compared the quality of whole-brain ASL acquired using three available head coils with differing number of receive channels (i.e., 20, 32, and 64ch). We found that using higher coil counts (32 and 64ch coils as compared to 20ch) offers improved signal-to-noise ratio (SNR) and acceleration capabilities that are beneficial for ASL imaging at 3 Tesla (3 T). The inherent reduction in partial voluming effects with higher resolution acquisitions improves the resolving power of perfusion without impacting the sensitivity. In conclusion, our results suggest that high-resolution ASL (2 to 2.5 mm isotropic voxels) has the potential to become a new standard for perfusion imaging at 3 T and increase its adoption into clinical research and cognitive neuroscience applications.

Advances and Frontiers in the Application of Cognitive Neuroscience and Brain Science in Legal Practice

Advances and Frontiers in the Application of Cognitive Neuroscience and Brain Science in Legal Practice

The application of cognitive neuroscience to judicial models: recent progress and trends.

Legal prediction presents one of the most significant challenges when applying artificial intelligence (AI) to the legal field. The legal system is a complex adaptive system characterized by the ambiguity of legal language and the diversity of value functions. The imprecision and procedural knowledge inherent in law makes judicial issues difficult to be expressed in a computer symbol system. Current semantic processing and machine learning technologies cannot fully capture the complex nature of legal relations, thereby raising doubts about the accuracy of legal predictions and reliability of judicial models. Cognitive computing, designed to emulate human brain functions and aid in enhancing decision-making processes, offers a better understanding of legal data and the processes of legal reasoning. This paper discusses the advancements made in cognitive methods applied to legal concept learning, semantic extraction, judicial data processing, legal reasoning, understanding of judicial bias, and the interpretability of judicial models. The integration of cognitive neuroscience with law has facilitated several constructive attempts, indicating that the evolution of cognitive law could be the next frontier in the intersection of AI and legal practice.

Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models

We present generalized additive latent and mixed models (GALAMMs) for analysis of clustered data with responses and latent variables depending smoothly on observed variables. A scalable maximum likelihood estimation algorithm is proposed, utilizing the Laplace approximation, sparse matrix computation, and automatic differentiation. Mixed response types, heteroscedasticity, and crossed random effects are naturally incorporated into the framework. The models developed were motivated by applications in cognitive neuroscience, and two case studies are presented. First, we show how GALAMMs can jointly model the complex lifespan trajectories of episodic memory, working memory, and speed/executive function, measured by the California Verbal Learning Test (CVLT), digit span tests, and Stroop tests, respectively. Next, we study the effect of socioeconomic status on brain structure, using data on education and income together with hippocampal volumes estimated by magnetic resonance imaging. By combining semiparametric estimation with latent variable modeling, GALAMMs allow a more realistic representation of how brain and cognition vary across the lifespan, while simultaneously estimating latent traits from measured items. Simulation experiments suggest that model estimates are accurate even with moderate sample sizes.

Comparing BOLD and VASO-CBV population receptive field estimates in human visual cortex

Vascular Space Occupancy (VASO) is an alternative fMRI approach based on changes in Cerebral Blood Volume (CBV). VASO-CBV fMRI can provide higher spatial specificity than the blood oxygenation level-dependent (BOLD) method because the CBV response is thought to be limited to smaller vessels. To investigate how this technique compares to BOLD fMRI for cognitive neuroscience applications, we compared population receptive field (pRF) mapping estimates between BOLD and VASO-CBV. We hypothesized that VASO-CBV would elicit distinct pRF properties compared to BOLD. Specifically, since pRF size estimates also depend on vascular sources, we hypothesized that reduced vascular blurring might yield narrower pRFs for VASO-CBV measurements. We used a VASO sequence with a double readout 3D EPI sequence at 7T to simultaneously measure VASO-CBV and BOLD responses in the visual cortex while participants viewed conventional pRF mapping stimuli. Both VASO-CBV and BOLD images show similar eccentricity and polar angle maps across all participants. Compared to BOLD-based measurements, VASO-CBV yielded lower tSNR and variance explained. The pRF size changed with eccentricity similarly for VASO-CBV and BOLD, and the pRF size estimates were similar for VASO-CBV and BOLD, even when we equate variance explained between VASO-CBV and BOLD. This result suggests that the vascular component of the pRF size is not dominating in either VASO-CBV or BOLD.

Mechanical thoughts and applications in cognitive neuroscience

This review article systematically summarizes the neural energy theory and methods proposed by our team in the field of brain science, and the internal relationship between mechanics and neural energy theory. This paper introduces how to construct an equivalent W-Z neuron model with the H-H model using the idea of analytic dynamics. Based on this, a large-scale neural model with neural energy as the core and a theoretical framework of global neural coding are proposed in the field of neuroscience. The unique functions and advantages of this novel neuron model are confirmed in the aspects of information processing, including visual perception, brain intelligence exploration, prediction of new working mechanisms of neurons and explanation of experimental phenomena challenging to explain in neuroscience. Because plasticity is the core of cognitive neuroscience and intelligent behavior, through the classical mechanical analysis of protein molecular machines, it is further clarified that the plasticity and neurodevelopment of neurons are not only biochemical reaction processes but also the role and contribution of mechanics are indispensable and important factors. It shows that the research thought of mechanics science in neuroscience and life science and its profound influence on internal logic. These studies will promote the integration of experimental neuroscience and theoretical neuroscience in the future, abandon the shortcomings in the research methods of reductionism and holism in the field of neuroscience, and integrate their respective advantages effectively. It is extremely important to promote the penetration of theories and methods of mechanical science.

Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T

Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 ​T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 ​mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit.

Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach

We study the problem of recovery of matrices that are simultaneously low rank and row and/or column sparse. Such matrices appear in recent applications in cognitive neuroscience, imaging, computer vision, macroeconomics, and genetics. We propose a GDT (Gradient Descent with hard Thresholding) algorithm to efficiently recover matrices with such structure, by minimizing a bi-convex function over a nonconvex set of constraints. We show linear convergence of the iterates obtained by GDT to a region within statistical error of an optimal solution. As an application of our method, we consider multi-task learning problems and show that the statistical error rate obtained by GDT is near optimal compared to minimax rate. Experiments demonstrate competitive performance and much faster running speed compared to existing methods, on both simulations and real data sets.

Efficient Projection onto the $\ell_{\infty,1}$ Mixed-Norm Ball Using a Newton Root Search Method

Mixed norms that promote structured sparsity have numerous applications in signal processing and machine learning problems. In this work, we present a new algorithm, based on a Newton root search technique, for computing the projection onto the $\ell_{\infty,1}$ ball, which has found application in cognitive neuroscience and classification tasks. Numerical simulations show that our proposed method is between 8 and 10 times faster on average, and up to 20 times faster for very sparse solutions, than the previous state of the art. Tests on real functional magnetic resonance image data show that, for some data distributions, our algorithm can obtain speed improvements by a factor of between 10 and 100, depending on the implementation.

Application of Cognitive Neuroscience in Color Composition in Graphic Design

Color as a unique sentience human has is ubiquitous in graphic design and art. It can make graphic design more gorgeous with an ingenious idea. This paper aims to discover the relationship between color composition and cognitive neuroscience in attempt to fully exert the advantages of colors in graphic design. Here the important role of colors in graphic design and the relationship between color composition and cognitive neuroscience are first elaborated. Next, several impact factors affecting graphic design is blended with the color concept to explore how they impact each other.

Brain computer interfaces for the industrial application of cognitive neuroscience

Brain computer interfaces for the industrial application of cognitive neuroscience

A novel approach to identify time-frequency oscillatory features in electrocortical signals

BackgroundSensory, motor, and cognitive events could not only evoke phase-locked event-related potentials in ongoing electrocortical signals, but also induce non-phase-locked changes of oscillatory activities. These oscillatory activities, whose functional significances differ greatly according to their temporal, spectral, and spatial characteristics, are commonly detected when single-trial signals are transformed into time-frequency distributions (TFDs). Parameters characterizing oscillatory activities are normally measured from multi-channel TFDs within a time-frequency region-of-interest (TF-ROI), pre-defined using a hypothesis-driven or data-driven approach. However, both approaches could ignore the possibility that the pre-defined TF-ROI contains several spatially/functionally distinct oscillatory activities. New methodWe proposed a novel approach based on topographic segmentation analysis to optimally and automatically identify detailed time-frequency features. This approach, which could effectively exploit the spatial information of oscillatory activities, has been validated in both simulation and real electrocortical studies. ResultsSimulation study showed that the proposed approach could successfully identify noise-contaminated time-frequency features if their signal-to-noise ratio was relatively high. Real electrocortical study demonstrated that several time-frequency features with distinct scalp distributions and evident neurophysiological functions were identified when the same analysis was applied on stimulus-elicited TFDs. Comparison with existing methodsUnlike traditional approaches, the proposed approach could provide an optimal identification of detailed time-frequency features by making use of their distinct spatial distributions. ConclusionsOur findings illustrated the validity and usefulness of the presented approach in isolating detailed time-frequency features, thus having wide applications in cognitive neuroscience to provide a precise assessment of the functional significance of oscillatory activities

A Novel 16-Channel Wireless System for Electroencephalography Measurements With Dry Spring-Loaded Sensors

Understanding brain function using electroencephalography (EEG) is an important issue for cerebral nervous system diseases, especially for epilepsy and Alzheimer's disease. Many EEG measurement systems are used reliably to study these diseases, but their bulky size and the use of wet sensors make them uncomfortable and inconvenient for users. To overcome the limitations of conventional EEG measurement systems, a wireless and wearable multichannel EEG measurement system is proposed in this paper. This system includes a wireless data acquisition device, dry spring-loaded sensors, and a size-adjustable soft cap. We compared the performance of the proposed system using dry versus conventional wet sensors. A significant positive correlation between readings from wet and dry sensors was achieved, thus demonstrating the performance of the system. Moreover, four different features of EEG signals (i.e., normal, eye-blinking, closed-eyes, and teeth-clenching signals) were measured by 16 dry sensors to ensure that they could be detected in real-life cognitive neuroscience applications. Thus, we have shown that it is possible to reliably measure EEG signals using the proposed system. This paper presents novel insights into the field of cognitive neuroscience, showing the possibility of studying brain function under real-life conditions.

The potential relevance of cognitive neuroscience for the development and use of technology-enhanced learning

There is increasing interest in the application of cognitive neuroscience in educational thinking and practice, and here we review findings from neuroscience that demonstrate its potential relevance to technology-enhanced learning (TEL). First, we identify some of the issues in integrating neuroscientific concepts into TEL research. We caution against seeking prescriptive neuroscience solutions for TEL and emphasize the need, instead, to conceptualize TEL at several different levels of analysis (brain, mind and behaviour, including social behaviour). Our review emphasizes the possibility of combining TEL and neuroscience concepts in adaptive educational systems, and we consider instances of interdisciplinary technology-based interventions drawing on neuroscience and aimed at remediating developmental disorders. We also consider the potential relevance of findings from neuroscience for the development of artificial agency, creativity, collaborative learning and neural insights into how different types of multimodality may influence learning, which may have implications for the future developments of tangibles. Finally, we identify a range of reasons why dialogue between neuroscience and the communities involved with technology and learning is likely to increase in the future.

Non-linear and non-stationary resting state functional connectivity examined using fast (sub-second) functional MRI.

Event Abstract Back to Event Non-linear and non-stationary resting state functional connectivity examined using fast (sub-second) functional MRI. Bryan Paton1, 2, 3, 4*, Parnesh Raniga1, 5 and Gary Egan1, 2 1 Monash University, Monash Biomedical Imaging, Australia 2 Monash University, School of Psychology & Psychiatry, Australia 3 Monash University, Philosophy & Cognition Lab, SOPHIS, Australia 4 Monash University, tLab, Australia 5 The Australian e-Health Research Centre-BioMedIA, CSIRO Preventative Health National Research Flagship ICTC, Australia Background: Functional connectivity (FC) analyses of functional MRI resting state data has revealed a robust set of spatial networks of intrinsic neural activity. Echo planar imaging (EPI), the primary sequence for acquiring fMRI data, has a limited whole brain imaging temporal resolution of 2-3 seconds that limits the investigation of network dynamics. Recent fast EPI sequences that decrease the minimum EPI scanning time to less than one second include the MREG and Multiband EPI sequences. Accelerated whole brain fMRI, up to ten times the temporal resolution of standard EPI, permits dynamic network analyses not previously possible. Our aim was to examine non-linear and non-stationary resting state functional connectivity using fast EPI methods and compare them to typical unaccelerated EPI data. Method: Whole brain fMRI resting state data using was collected (TR: 650 ms, TE: 30 ms, Flip Angle: 30°, 44 slices, 64 x 64 matrix, 3mm isotropic voxels, Multiband acceleration: x4) from healthy controls using the Human Connectome Project Multiband EPI sequence. We compared a number of correlational measures including cross-correlation, Kullback–Leibler divergence and sample-entropy based statistics with and without windowing. Graph theoretical network measures were derived from the resulting FC matrices. Results: Contrary to typical resting state analyses, FC derived from Multiband EPI sequences shows changes in state and configuration over both short (less than 20 s) and long (greater than 60 s) time scales. The results highlight that the assumptions of stationarity and linearity for both task related and resting state analyses are violated using fast EPI methods. Discussion: Fast EPI sequences are now available for the investigation of resting state and task-related FC applications in cognitive neuroscience. Whilst assuming that resting state data are stationary, linear and phase consistent as is the case with cross-correlation based functional connectivity, the results of fast EPI methods reveal a deeper and richer non-linear, non-stationary structure. The temporal sampling resolution now achievable using fast EPI enables novel studies investigating the dynamics of resting state and potentially task related cognitive processes. Keywords: multiband epi, functional connectivity, fMRI, non-linear, Non-stationary Conference: ACNS-2013 Australasian Cognitive Neuroscience Society Conference, Clayton, Melbourne, Australia, 28 Nov - 1 Dec, 2013. Presentation Type: Poster Topic: Other Citation: Paton B, Raniga P and Egan G (2013). Non-linear and non-stationary resting state functional connectivity examined using fast (sub-second) functional MRI.. Conference Abstract: ACNS-2013 Australasian Cognitive Neuroscience Society Conference. doi: 10.3389/conf.fnhum.2013.212.00056 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 15 Oct 2013; Published Online: 25 Nov 2013. * Correspondence: Mr. Bryan Paton, Monash University, Monash Biomedical Imaging, Clayton, VIC, 3168, Australia, bryan.paton@newcastle.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Bryan Paton Parnesh Raniga Gary Egan Google Bryan Paton Parnesh Raniga Gary Egan Google Scholar Bryan Paton Parnesh Raniga Gary Egan PubMed Bryan Paton Parnesh Raniga Gary Egan Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging

Auditory cortex pertains to the processing of sound, which is at the basis of speech or music-related processing1. However, despite considerable recent progress, the functional properties and lateralization of the human auditory cortex are far from being fully understood. Transcranial Magnetic Stimulation (TMS) is a non-invasive technique that can transiently or lastingly modulate cortical excitability via the application of localized magnetic field pulses, and represents a unique method of exploring plasticity and connectivity. It has only recently begun to be applied to understand auditory cortical function 2. An important issue in using TMS is that the physiological consequences of the stimulation are difficult to establish. Although many TMS studies make the implicit assumption that the area targeted by the coil is the area affected, this need not be the case, particularly for complex cognitive functions which depend on interactions across many brain regions 3. One solution to this problem is to combine TMS with functional Magnetic resonance imaging (fMRI). The idea here is that fMRI will provide an index of changes in brain activity associated with TMS. Thus, fMRI would give an independent means of assessing which areas are affected by TMS and how they are modulated 4. In addition, fMRI allows the assessment of functional connectivity, which represents a measure of the temporal coupling between distant regions. It can thus be useful not only to measure the net activity modulation induced by TMS in given locations, but also the degree to which the network properties are affected by TMS, via any observed changes in functional connectivity. Different approaches exist to combine TMS and functional imaging according to the temporal order of the methods. Functional MRI can be applied before, during, after, or both before and after TMS. Recently, some studies interleaved TMS and fMRI in order to provide online mapping of the functional changes induced by TMS 5-7. However, this online combination has many technical problems, including the static artifacts resulting from the presence of the TMS coil in the scanner room, or the effects of TMS pulses on the process of MR image formation. But more importantly, the loud acoustic noise induced by TMS (increased compared with standard use because of the resonance of the scanner bore) and the increased TMS coil vibrations (caused by the strong mechanical forces due to the static magnetic field of the MR scanner) constitute a crucial problem when studying auditory processing. This is one reason why fMRI was carried out before and after TMS in the present study. Similar approaches have been used to target the motor cortex 8,9, premotor cortex 10, primary somatosensory cortex 11,12 and language-related areas 13, but so far no combined TMS-fMRI study has investigated the auditory cortex. The purpose of this article is to provide details concerning the protocol and considerations necessary to successfully combine these two neuroscientific tools to investigate auditory processing. Previously we showed that repetitive TMS (rTMS) at high and low frequencies (resp. 10 Hz and 1 Hz) applied over the auditory cortex modulated response time (RT) in a melody discrimination task 2. We also showed that RT modulation was correlated with functional connectivity in the auditory network assessed using fMRI: the higher the functional connectivity between left and right auditory cortices during task performance, the higher the facilitatory effect (i.e. decreased RT) observed with rTMS. However those findings were mainly correlational, as fMRI was performed before rTMS. Here, fMRI was carried out before and immediately after TMS to provide direct measures of the functional organization of the auditory cortex, and more specifically of the plastic reorganization of the auditory neural network occurring after the neural intervention provided by TMS. Combined fMRI and TMS applied over the auditory cortex should enable a better understanding of brain mechanisms of auditory processing, providing physiological information about functional effects of TMS. This knowledge could be useful for many cognitive neuroscience applications, as well as for optimizing therapeutic applications of TMS, particularly in auditory-related disorders.

Mapping the After-effects of Theta Burst Stimulation on the Human Auditory Cortex with Functional Imaging

Auditory cortex pertains to the processing of sound, which is at the basis of speech or music-related processing. However, despite considerable recent progress, the functional properties and lateralization of the human auditory cortex are far from being fully understood. Transcranial Magnetic Stimulation (TMS) is a non-invasive technique that can transiently or lastingly modulate cortical excitability via the application of localized magnetic field pulses, and represents a unique method of exploring plasticity and connectivity. It has only recently begun to be applied to understand auditory cortical function. An important issue in using TMS is that the physiological consequences of the stimulation are difficult to establish. Although many TMS studies make the implicit assumption that the area targeted by the coil is the area affected, this need not be the case, particularly for complex cognitive functions which depend on interactions across many brain regions. One solution to this problem is to combine TMS with functional Magnetic resonance imaging (fMRI). The idea here is that fMRI will provide an index of changes in brain activity associated with TMS. Thus, fMRI would give an independent means of assessing which areas are affected by TMS and how they are modulated. In addition, fMRI allows the assessment of functional connectivity, which represents a measure of the temporal coupling between distant regions. It can thus be useful not only to measure the net activity modulation induced by TMS in given locations, but also the degree to which the network properties are affected by TMS, via any observed changes in functional connectivity. Different approaches exist to combine TMS and functional imaging according to the temporal order of the methods. Functional MRI can be applied before, during, after, or both before and after TMS. Recently, some studies interleaved TMS and fMRI in order to provide online mapping of the functional changes induced by TMS. However, this online combination has many technical problems, including the static artifacts resulting from the presence of the TMS coil in the scanner room, or the effects of TMS pulses on the process of MR image formation. But more importantly, the loud acoustic noise induced by TMS (increased compared with standard use because of the resonance of the scanner bore) and the increased TMS coil vibrations (caused by the strong mechanical forces due to the static magnetic field of the MR scanner) constitute a crucial problem when studying auditory processing. This is one reason why fMRI was carried out before and after TMS in the present study. Similar approaches have been used to target the motor cortex, premotor cortex, primary somatosensory cortex and language-related areas, but so far no combined TMS-fMRI study has investigated the auditory cortex. The purpose of this article is to provide details concerning the protocol and considerations necessary to successfully combine these two neuroscientific tools to investigate auditory processing. Previously we showed that repetitive TMS (rTMS) at high and low frequencies (resp. 10 Hz and 1 Hz) applied over the auditory cortex modulated response time (RT) in a melody discrimination task. We also showed that RT modulation was correlated with functional connectivity in the auditory network assessed using fMRI: the higher the functional connectivity between left and right auditory cortices during task performance, the higher the facilitatory effect (i.e. decreased RT) observed with rTMS. However those findings were mainly correlational, as fMRI was performed before rTMS. Here, fMRI was carried out before and immediately after TMS to provide direct measures of the functional organization of the auditory cortex, and more specifically of the plastic reorganization of the auditory neural network occurring after the neural intervention provided by TMS. Combined fMRI and TMS applied over the auditory cortex should enable a better understanding of brain mechanisms of auditory processing, providing physiological information about functional effects of TMS. This knowledge could be useful for many cognitive neuroscience applications, as well as for optimizing therapeutic applications of TMS, particularly in auditory-related disorders.

Noninvasive Human Brain Stimulation

Noninvasive brain stimulation with transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS) is valuable in research and has potential therapeutic applications in cognitive neuroscience, neurophysiology, psychiatry, and neurology. TMS allows neurostimulation and neuromodulation, while tDCS is a purely neuromodulatory application. TMS and tDCS allow diagnostic and interventional neurophysiology applications, and focal neuropharmacology delivery. However, the physics and basic mechanisms of action remain incompletely explored. Following an overview of the history and current applications of noninvasive brain stimulation, we review stimulation device design principles, the electromagnetic and physical foundations of the techniques, and the current knowledge about the electrophysiologic basis of the effects. Finally, we discuss potential biomedical and electrical engineering developments that could lead to more effective stimulation devices, better suited for the specific applications.

Applying New Approaches From Cognitive Neuroscience to Enhance Drug Development for the Treatment of Impaired Cognition in Schizophrenia

New approaches to the measurement of cognition in schizophrenia include the use of tasks from experimental cognitive psychology to examine the integrity of specific cognitive systems and the application of these tasks in noninvasive neuroimaging (e.g., functional magnetic resonance imaging [fMRI]) studies that directly measure the effects of drugs on cognition-related brain activity. These approaches offer many advantages, including the isolation of specific cognitive systems that may be conserved across species; controlling for the confounding effects of generalized performance deficits such as poor motivation, sedation, and so on; and providing a direct translational bridge from studies using animal models of cognition to patient-based research using fMRI. These developments have the potential to transform the early human phases of drug development and streamline the decision making at this critical point in the process. As was the case for the Measurement and Treatment Research to Improve Cognition in Schizophrenia initiative, optimizing the application of cognitive neuroscience to new drug development will require a major commitment by multiple investigators to task development and a thorough psychometric evaluation of both behavioral and neuroimaging measures.