Neuroscience Data Research Articles

Refining the Common Model of Cognition Through Large Neuroscience Data

The Common Model of Cognition (CMC) is an effort to highlight the commonalities between multiple studies of human-like intelligent minds and cognition, and bring these architectures and functions together into one common model. The CMC is still relatively new, and while it has substantial theoretical evidence in its favor, there is little empirical evidence confirming the theory. The CMC is fundamentally informed by human cognition, and must at least hold true for human brains. As such, this paper uses a large fMRI dataset from the Human Connectome Project (HCP) to refine the CMC and test it as a reasonable model for human cognition. We tested three models in the CMC family and two alternative models against two tasks from the HCP dataset. We found that one model from the CMC family explained the HCP dataset best, providing further empirical evidence in favor of the CMC, whilst also suggesting a slight modification to the CMC itself that may improve the model’s generalizability.

Mining Neuroscience Data for Social Campaign Evaluation

Modern technology allows to gather large amount of data. This applies to every aspect of life. Scientific research is not an exception in that matter. It is worth noting that this applies not only to the areas that have always been associated with a large amount of data (astronomy, weather forecasting, etc.), but also those that have only recently gained new possibilities of acquiring such quantities. These include social sciences primarily involving the collecting data from people and about people. They are no longer limited to surveys and interviews, but, using the new technologies, they go much deeper - not only to the conscious declarations of the participants, but also to their subconscious opinions and beliefs. In this context, cognitive neuroscience techniques appear. Thanks to the use of tools previously associated mainly with medicine, they allow you to gain new information on people’s preferences, feelings, unconscious opinions, etc. However, in order for this information to be extracted, data collected with the use of such tools must be treated with a number of algorithms that will help to extract what is most important. In order to achieve this, data mining methods are used. However, the conducted procedure must be adapted to the specifics of this data. The aim of the article is to characterize the data that is collected using the cognitive neuroscience techniques, presenting the following stages of its processing and analysis, and methods that are commonly used, so that it is possible to obtain valuable information that will allow the researchers to draw the right conclusions. Such procedure will be presented on the example of research on measuring the effectiveness of media messages in social campaigns.

Multi-Scale Peak and Trough Detection Optimised for Periodic and Quasi-Periodic Neuroscience Data.

The reliable detection of peaks and troughs in physiological signals is essential to many investigative techniques in medicine and computational biology. Analysis of the intracranial pressure (ICP) waveform is a particular challenge due to multi-scale features, a changing morphology over time and signal-to-noise limitations. Here we present an efficient peak and trough detection algorithm that extends the scalogram approach of Scholkmann et al., and results in greatly improved algorithm runtime performance. Our improved algorithm (modified Scholkmann) was developed and analysed in MATLAB R2015b. Synthesised waveforms (periodic, quasi-periodic and chirp sinusoids) were degraded with white Gaussian noise to achieve signal-to-noise ratios down to 5 dB and were used to compare the performance of the original Scholkmann and modified Scholkmann algorithms. The modified Scholkmann algorithm has false-positive (0%) and false-negative (0%) detection rates identical to the original Scholkmann when applied to our test suite. Actual compute time for a 200-run Monte Carlo simulation over a multicomponent noisy test signal was 40.96 ± 0.020 s (mean ± 95%CI) for the original Scholkmann and 1.81 ± 0.003 s (mean ± 95%CI) for the modified Scholkmann, demonstrating the expected improvement in runtime complexity from [Formula: see text] to [Formula: see text]. The accurate interpretation of waveform data to identify peaks and troughs is crucial in signal parameterisation, feature extraction and waveform identification tasks. Modification of a standard scalogram technique has produced a robust algorithm with linear computational complexity that is particularly suited to the challenges presented by large, noisy physiological datasets. The algorithm is optimised through a single parameter and can identify sub-waveform features with minimal additional overhead, and is easily adapted to run in real time on commodity hardware.

How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database.

Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines (behavioural neuroscience, genetics, cognitive psychology, etc.) working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest (RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer's disease (AD), the conversion from mild cognitive impairment (MCI) and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1st position in an international challenge for automated prediction of MCI from MRI data.

Mentalizacijos paauglystėje raida ir sutrikimai

Paauglystė – tai laikotarpis, kai vyksta itin daug pokyčių, ypač svarbių socialiniam pažinimui. Tai nulemia vis geresnį paauglių savęs įsisąmoninimą (Harter, 2012). Todėl šiuo laikotarpiu labai svarbus tampa besivystantis mentalizacijos gebėjimas, užtikrinantis savo ir kitų elgesio supratimą psichikos būsenų kontekste (Fonagy, Gergely, Jurist, & Target, 2005). Nors neabejojama paauglystės laikotarpio svarba įtvirtinant mentalizacijos gebėjimą, vis dar stokojama nuoseklios mentalizacijos raidos paauglystėje analizės. Šiame straipsnyje* siekiama užpildyti esamą spragą ir, remiantis psichodinamine, raidos psichologijos, raidos psichopatologijos bei neuromokslo teorine ir empirine medžiaga, analizuoti mentalizacijos gebėjimo raidą paauglystėje, pagrindinį dėmesį skiriant paauglystėje svarbiausioms poliariškoms mentalizacijos konstrukto dimensijoms – emocinei ir kognityviai bei savęs ir kito mentalizacijai, taip pat mentalizacijos sutrikimo sąsajai su psichopatologija.

Improving resolution of dynamic communities in human brain networks through targeted node removal.

Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined “ground truth” communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters.

Within-subject mediation analysis for experimental data in cognitive psychology and neuroscience.

Statistical mediation allows researchers to investigate potential causal effects of experimental manipulations through intervening variables. It is a powerful tool for assessing the presence and strength of postulated causal mechanisms. Although mediation is used in certain areas of psychology, it is rarely applied in cognitive psychology and neuroscience. One reason for the scarcity of applications is that these areas of psychology commonly employ within-subjects designs, and mediation models for within-subjects data are considerably more complicated than for between-subjects data. Here, we draw attention to the importance and ubiquity of mediational hypotheses in within-subjects designs, and we present a general and flexible software package for conducting Bayesian within-subjects mediation analyses in the R programming environment. We use experimental data from cognitive psychology to illustrate the benefits of within-subject mediation for theory testing and comparison.

The Role of Neuroscience in the Evaluation of Mental Insanity: on the Controversies in Italy

In the present manuscript, we comment upon a paper that strongly criticized an expert report written by the consultants of the defense (two of the authors of the present paper, PP and GS) in a case of pedophilia, in which clinical and neuro-scientific data were used to establish the causal link between brain alterations and onset of criminal behavior. These critiques appear to be based mainly on wrong pieces of information and on a misinterpretation of the logical reasoning adopted by defense consultants. Here we provide a point-by-point reply to the issues raised in the above paper and also discuss the potential role that neuroscience may contribute in the forensic context. Did the forensic neuroscience defense consultants claim the existence of a deterministic relationship between brain structure or function and behavior? How did the neuroscientific logic work in this specific case? How may the classic psychiatric/neurologic examination and neuroscientific evidence work side by side? Does the rarity of a disease impact on the causal relationship between the disease and the crime? Do neuroscientific data need to be interpreted? We address the above questions and conclude that neuroscience may strengthen the results of psychiatric evaluations, thus reducing uncertainty in the forensic settings.

Glossolalia and Aphasia: Related but Different Worlds.

The word glossolalia, also referred to as "speaking in tongues," originates from the Greek "glossa" which means "language" and "Lalia" which means "speak." It simply means to talk language. On a linguistic perspective, glossolalia is characterized by almost no recognizable words or semantic content, apart from biblical words and phrases, with an overrepresentation of a small phonemes number, accelerated speech output, and modification of accents and melody. Its phonemic properties have been said to resemble those of the language(s) of the speaker. It is generally a voluntary intimate communication act associated with religious spiritual thoughts, although it has also been described in mental disorders. Glossolalia state is a special mental state, completely different from aphasia. Aphasia is characterized by some phonological and grammatical rules; however, these are related to neurolinguistics and not to developmental principles. Unlike aphasia, glossolalia is not associated with central nervous system affection. There is, however, a psychiatric type of glossolalia, which, although similar in terms of phenomenology, is probably a different phenomenon. A common thought is that there is a linguistic trend, whose basic component is a stream of speech. This stream is suggested to use early-acquired rules of phonation, adapted according to socially meaningful values and attitudes, in an unconscious way. Therefore, the act of glossolalia is out of the "glossolalists" control. It would appear, therefore, that there is a broad spectrum of anomalous speech, of which religious glossolalia is only one manifestation. The aim of this chapter is to understand the mental state of glossolalia in comparison to aphasia in the light of neurolinguistic and psychiatric features. Moreover, we tried to highlight the underlying brain network. Pilot neuroscientific data suggest that it is associated with a deactivation of the cognitive system and an activation of some parts of the mirror neuron networks.

A Tutorial Review on Multi-subject Decomposition of EEG.

Over the last years we saw a steady increase in the relevance of big neuroscience data sets, and with it grew the need for analysis tools capable of handling such large data sets while simultaneously extracting properties of brain activity that generalize across subjects. For functional magnetic resonance imaging, multi-subject or group-level independent component analysis provided a data-driven approach to extract intrinsic functional networks, such as the default mode network. Meanwhile, this methodological framework has been adapted for the analysis of electroencephalography (EEG) data. Here, we provide an overview of the currently available approaches for multi-subject data decomposition as applied to EEG, and highlight the characteristics of EEG that warrant special consideration. We further illustrate the importance of matching one's choice of method to the data characteristics at hand by guiding the reader through a set of simulations. In sum, algorithms for group-level decomposition of EEG provide an innovative and powerful tool to study the richness of functional brain networks in multi-subject EEG data sets.

Self-Control as Value-Based Choice

Self-control is often conceived as a battle between “hot” impulsive processes and “cold” deliberative ones. Heeding the angel on one shoulder leads to success; following the demon on the other leads to failure. Self-control feels like a duality. What if that sensation is misleading, and despite how they feel, self-control decisions are just like any other choice? We argue that self-control is a form of value-based choice wherein options are assigned a subjective value and a decision is made through a dynamic integration process. We articulate how a value-based choice model of self-control can capture its phenomenology and account for relevant behavioral and neuroscientific data. This conceptualization of self-control links divergent scientific approaches, allows for more robust and precise hypothesis testing, and suggests novel pathways to improve self-control.

Isabelle Rapin (1927–2017)

Isabelle Rapin (1927–2017)

A new look at domain specificity: insights from social neuroscience.

The concept of domain specificity - which suggests that some aspects of neural processing are specialized for particular types of stimuli - has been invoked to explain a range of cognitive phenomena, including language, face perception and theory of mind, and has been a hallmark of theories of cognitive architecture. More recent usage of this concept draws on neuroscientific data and, in particular, on work in social neuroscience. A critical examination of the part that the concept of domain specificity has played in theories of human brain function leads us to suggest a new view according to which domain specificity pertains to centrally generated constraints on information processing that can be both dynamic and context sensitive.

Empathy-from Brain Correlation to Mind and Behavior Cultivation: Bridging Neuroscience, Psychology and Philosophy

In this paper, the flow of the argument follows the sequence of the areas of studies alluded to in the title. That is, I start with some neuroscience data relative to empathy, then I address the topic from a psychological perspective and I close with a section that tackles a philosophical perspective. As such, I begin illustrating the neural correlations of empathy, drawing on a couple of studies conducted by renowned scientists such as Simon-Baron Cohen, Jean Decety and Joseph LeDoux. Based on this knowledge of the correlations between the brain and empathy, I go on to demonstrate the importance of cultivating these correlations cognitively, affectively and behaviorally (through action) so that we (human beings) develop and strengthen an empathic mind. This is where developmental psychology steps in. Adding the mental development to the physiological/neural knowledge creates the channel to expressing empathic behavior; thus leading to the section about ethics or philosophy. I conclude by stressing on the powerful role of empathy in transforming societies for the better, given its phenomenal characteristic of bridging between different sciences.

Mammalian empathy: behavioural manifestations and neural basis.

Recent research on empathy in humans and other mammals seeks to dissociate emotional and cognitive empathy. These forms, however, remain interconnected in evolution, across species and at the level of neural mechanisms. New data have facilitated the development of empathy models such as the perception-action model (PAM) and mirror-neuron theories. According to the PAM, the emotional states of others are understood through personal, embodied representations that allow empathy and accuracy to increase based on the observer's past experiences. In this Review, we discuss the latest evidence from studies carried out across a wide range of species, including studies on yawn contagion, consolation, aid-giving and contagious physiological affect, and we summarize neuroscientific data on representations related to another's state.

Inferential and referential lexical semantic competence: A critical review of the supporting evidence

Abstract In philosophical semantics, a distinction has been proposed between inferential and referential lexical semantic competence. The former accounts for the relationship of words to the world, the latter for the relationship of words among themselves. Recent neuroscience research suggests that the distinction might be actually neurally implemented. That is, that inferential and referential abilities might be underpinned by two functionally independent cognitive architectures, with partly different neural realizations. This hypothesis is consistent with brain patient data, supporting the notion of a functional double dissociation between inferential and referential abilities, and with neuroscience data, suggesting that inferential and referential abilities are underpinned by at least partly different regions of the human brain. The principal aim of this article is to provide the first comprehensive and critical review of the empirical evidence in favour of such hypothesis.

Analytical Derivation of Nonlinear Spectral Effects and 1/f Scaling Artifact in Signal Processing of Real-World Data.

In estimating the frequency spectrum of real-world time series data, we must violate the assumption of infinite-length, orthogonal components in the Fourier basis. While it is widely known that care must be taken with discretely sampled data to avoid aliasing of high frequencies, less attention is given to the influence of low frequencies with period below the sampling time window. Here, we derive an analytic expression for the side-lobe attenuation of signal components in the frequency domain representation. This expression allows us to detail the influence of individual frequency components throughout the spectrum. The first consequence is that the presence of low-frequency components introduces a 1/f[Formula: see text] component across the power spectrum, with a scaling exponent of [Formula: see text]. This scaling artifact could be composed of diffuse low-frequency components, which can render it difficult to detect a priori. Further, treatment of the signal with standard digital signal processing techniques cannot easily remove this scaling component. While several theoretical models have been introduced to explain the ubiquitous 1/f[Formula: see text] scaling component in neuroscientific data, we conjecture here that some experimental observations could be the result of such data analysis procedures.

Communicative-Pragmatic Assessment Is Sensitive and Time-Effective in Measuring the Outcome of Aphasia Therapy.

A range of methods in clinical research aim to assess treatment-induced progress in aphasia therapy. Here, we used a crossover randomized controlled design to compare the suitability of utterance-centered and dialogue-sensitive outcome measures in speech-language testing. Fourteen individuals with post-stroke chronic non-fluent aphasia each received two types of intensive training in counterbalanced order: conventional confrontation naming, and communicative-pragmatic speech-language therapy (Intensive Language-Action Therapy, an expanded version of Constraint-Induced Aphasia Therapy). Motivated by linguistic-pragmatic theory and neuroscience data, our dependent variables included a newly created diagnostic instrument, the Action Communication Test (ACT). This diagnostic instrument requires patients to produce target words in two conditions: (i) utterance-centered object naming, and (ii) communicative-pragmatic social interaction based on verbal requests. In addition, we administered a standardized aphasia test battery, the Aachen Aphasia Test (AAT). Composite scores on the ACT and the AAT revealed similar patterns of changes in language performance over time, irrespective of the treatment applied. Changes in language performance were relatively consistent with the AAT results also when considering both ACT subscales separately from each other. However, only the ACT subscale evaluating verbal requests proved to be successful in distinguishing between different types of training in our patient sample. Critically, testing duration was substantially shorter for the entire ACT (10–20 min) than for the AAT (60–90 min). Taken together, the current findings suggest that communicative-pragmatic methods in speech-language testing provide a sensitive and time-effective measure to determine the outcome of aphasia therapy.

The Role of Emotion Modulation in Moral Bioenhancement Debate

This paper aims to analyze technical and internal aspects of one particular type of human moral enhancement, i.e. enhancement of moral motivation via direct emotion modulation. More precisely, it challenges the assumption that modifying certain emotions will have the results desired by the advocates of this theory. It is argued that neuropsychological understanding of the role and function of emotions, as well as of underlying cognitive mechanisms, might be relevant for the discussion about biomedical enhancement of moral capacities. Moreover, typical claims about direct emotion modulation seem to be contradicted, or at least seriously challenged, by available neuroscientific data. Particular attention is paid to the theory that emotions are evolved and functionally specialized programs whose task is to coordinate other adaptive mechanisms of human psychology in order to promote one’s fitness. If this view of emotions is plausible, it can be argued that several difficulties for moral bioenhancement theory ensue. Neuroscientific and evolutionary-psychological perspective seem to indicate that emotions don’t fulfill necessary requirements to serve as the vehicles of moral enhancement and it should, therefore, take into account role and function of entire cognitive modules associated with moral decision-making.

Brains are Important Too: Reply to Hall, Carter, and Barnett

The authors and I agree on many features of addiction, such as its developmental (versus pathological) nature. But because I rely on much of the same data as the Brain Disease Model of Addiction (BDMA), they seem to conflate my work with that of my opponents. Indeed they are generally skeptical of the use of neuroscientific data to help understand addiction, calling it "immature." Thus my work is also suspect. Hall and colleagues believe that it is impossible to look at neural and social processes at the same time, yet that is exactly what I do. I suggest that interdisciplinary approaches to addiction are crucial.