Functional Magnetic Resonance Data Research Articles

Dynamic Functional Connectivity Strength Within Different Frequency-Band in Schizophrenia.

As a complex psychiatric disorder, schizophrenia is interpreted as a “dysconnection” syndrome, which is linked to abnormal integrations in between distal brain regions. Recently, neuroimaging has been widely adopted to investigate how schizophrenia affects brain networks. Furthermore, some studies reported frequency dependence of the abnormalities of functional network in schizophrenia, however, dynamic functional connectivity with frequency dependence is rarely used to explore changes in the whole brain of patients with schizophrenia (SZ). Therefore, in the current study, dynamic functional connectivity strength (dFCS) was performed on resting-state functional magnetic resonance data from 96 SZ patients and 121 healthy controls (HCs) at slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz), and slow-2 (0.198–0.25 Hz) frequency bands and further assessed whether the altered dFCS was correlated to clinical symptoms in SZ patients. Results revealed that decreased dFCS of schizophrenia were found in salience, auditory, sensorimotor, visual networks, while increased dFCS in cerebellum, basal ganglia, and prefrontal networks were observed across different frequency bands. Specifically, the thalamus subregion of schizophrenic patients exhibited enhanced dynamic FCS in slow-5 and slow-4, while reduced in slow-3. Moreover, in slow-5 and slow-4, significant interaction effects between frequency and group were observed in the left calcarine cortex, the bilateral inferior orbitofrontal gyrus, and anterior cingulum cortex (ACC). Furthermore, the altered dFCS of insula, thalamus (THA), calcarine cortex, orbitofrontal gyrus, and paracentral lobule were partial correlated with clinical symptoms of SZ patients in slow-5 and slow-4 bands. These results demonstrate the abnormalities of dFCS in schizophrenia patients is rely on different frequency bands and may provide potential implications for exploring the neuropathological mechanism of schizophrenia.

Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

Contemporary neuroscience has embraced network science and dynamical systems to study the complex and self-organized structure of the human brain. Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role in the emergent brain dynamics is still lacking. The main reason is that estimating brain connectivity requires solving a formidable large-scale inverse problem from indirect and noisy measurements. Building on the dynamic causal modelling framework, the present study offers a novel method for estimating whole-brain effective connectivity from resting-state functional magnetic resonance data. To this purpose sparse estimation methods are adapted to infer the parameters of our novel model, which is based on a linearized, region-specific haemodynamic response function. The resulting algorithm, referred to as sparse DCM, is shown to compare favorably with state-of-the art methods when tested on both synthetic and real data. We also provide a graph-theoretical analysis on the whole-brain effective connectivity estimated using data from a cohort of healthy individuals, which reveals properties such as asymmetry in the connectivity structure as well as the different roles of brain areas in favoring segregation or integration.

Power Spectral Density Analysis of Long-Term Motor Recovery in Patients With Subacute Stroke

Background. Prognostic measures of long-term motor recovery are important in patients with stroke presenting with severe hemiplegia. Objective. We aimed to investigate whether initial power spectral density (PSD) analysis of resting-state functional magnetic resonance (fMRI) data can provide a sensitive prognostic predictor in patients with subacute stroke with severe hand disability. Methods. Twelve patients with good recovery, 14 patients with poor recovery, and 12 healthy subjects were included. PSD analysis was performed using resting-state fMRI data. Contralesional and ipsilesional PSD in the motor cortex were measured. Pearson correlation analysis was performed to assess a possible association between the difference in ipsilesional versus contralesional PSD and motor outcomes. A receiver operating characteristic (ROC) curve was constructed to estimate the discriminative value of the difference between the ipsilesional PSD and the contralesional PSD for good versus poor recovery. Results. There were no differences in PSD between the contralesional and ipsilesional hemispheres in the good recovery group (P = .77). In contrast, there were significant differences in PSD between the 2 hemispheres in the poor recovery group (P = .07). The difference in PSD between the 2 hemispheres had a positive correlation with post Brunnstrom stage scores. ROC analysis showed that the difference in PSD between the 2 hemispheres was sensitive in discriminating good versus poor recovery. Conclusion. The present study suggests that PSD in the motor cortex may be a sensitive predictor of late-onset motor recovery following stroke.

Transient Hypothyroidism During Lactation Arrests Myelination in the Anterior Commissure of Rats. A Magnetic Resonance Image and Electron Microscope Study.

Thyroid hormone deficiency at early postnatal ages affects the cytoarchitecture and function of neocortical and telencephalic limbic areas, leading to impaired associative memory and in a wide spectrum of neurological and mental diseases. Neocortical areas project interhemispheric axons mostly through the corpus callosum and to a lesser extent through the anterior commissure (AC), while limbic areas mostly project through the AC and hippocampal commissures. Functional magnetic resonance data from children with late diagnosed congenital hypothyroidism and abnormal verbal memory processing, suggest altered ipsilateral and contralateral telencephalic connections. Gestational hypothyroidism affects AC development but the possible effect of transient and chronic postnatal hypothyroidism, as occurs in late diagnosed neonates with congenital hypothyroidism and in children growing up in iodine deficient areas, still remains unknown. We studied AC development using in vivo magnetic resonance imaging and electron microscopy in hypothyroid and control male rats. Four groups of methimazole (MMI) treated rats were studied. One group was MMI-treated from postnatal day (P) 0 to P21; some of these rats were also treated with L-thyroxine (T4) from P15 to P21, as a model for early transient hypothyroidism. Other rats were MMI-treated from P0 to P150 and from embryonic day (E) 10 to P170, as a chronic hypothyroidism group. The results were compared with age paired control rats. The normalized T2 signal using magnetic resonance image was higher in MMI-treated rats and correlated with the number and percentage of myelinated axons. Using electron microscopy, we observed decreased myelinated axon number and density in transient and chronic hypothyroid rats at P150, unmyelinated axon number increased slightly in chronic hypothyroid rats. In MMI-treated rats, the myelinated axon g-ratio and conduction velocity was similar to control rats, but with a decrease in conduction delays. These data show that early postnatal transient and chronic hypothyroidism alters AC maturation that may affect the transfer of information through the AC. The alterations cannot be recovered after delayed T4-treatment. Our data support the neurocognitive delay found in late T4-treated children with congenital hypothyroidism.

Physiological noise reduction algorithms for fMRI data

This article describes various approaches for excluding and recognizing artifacts in functional magnetic resonance data. Similar methods can be applied to intractable artifacts, such as breathing and pulse artifacts, from white matter and cerebrospinal fluid pulsations. In this paper, using real experimental data various algorithms and approaches that can be applied not only to standard echo-planar mri-sequences, but also to ultra fast sequences are shown. Such methods make it possible for researchers to reveal only the actual human brain activity and more accurately talk about zones of functionality for certain cognitive tasks.

Giving others the option of choice: An fMRI study on low-cost cooperation

Successful social relationships require a consideration of a partner's thoughts and intentions. This aspect of social life is captured in the social mindfulness paradigm (SoMi task), in which participants make decisions that either limit or preserve options for their interaction partner's subsequent choice. Here we investigated the neural correlates of spontaneous socially mindful and unmindful behaviours. Functional magnetic resonance data were acquired from 47 healthy adolescents and young adults (age 16–27) as they completed the SoMi task. Being faced with socially relevant choices was associated with activity in the medial prefrontal cortex, anterior cingulate, caudate, and insula, which is consistent with prior neuroeconomical research. Importantly, socially mindful choices were associated with activity in the right parietal cortex and the caudate, whereas unmindful choices were associated with activity in the left prefrontal cortex. These neural findings were consistent with the behavioural preference for mindful choices, suggesting that socially mindful decisions are the basic inclination, whereas socially unmindful responses may require greater effort and control. Together, these results begin to uncover the neural correlates of socially mindful and unmindful choices, and illuminate the psychological processes involved in cooperative social behaviour.

Functional dysconnectivity of the limbic loop of frontostriatal circuits in first-episode, treatment-naive schizophrenia.

Frontostriatal circuits dysfunction has been implicated in the etiology and psychopathology of patients with schizophrenia (SZ). However, few studies have investigated SZ-related functional connectivity (FC) alterations in discrete frontostriatal circuits and their relationship with pathopsychology in first-episode schizophrenia (FESZ). The goal of this study was to identify dysfunctions in discrete frontostriatal circuits that are associated with key features of FESZ. To this end, a case-control, cross-sectional study was conducted, wherein resting-state (RS) functional magnetic resonance (fMRI) data were collected from 37 treatment-naïve FESZ patients and 29 healthy control (HC) subjects. Seed-based FC analyses were performed by placing six bilateral pairs of seeds within a priori defined subdivisions of the striatum. We observed significantly decreased FC for the FESZ group relative to the HC group [p < .05, family-wise error (FWE)-corrected] in the limbic loop, but not in the sensorimotor or associative loops, of frontostriatal circuitry. Moreover, bilaterally decreased inferior ventral striatum/nucleus accumbens (VSi)-dorsal anterior cingulate cortex (dACC) FC within the limbic loop correlated inversely with overall FESZ symptom severity and the disorganization factor score of PANSS. These findings provide new insight into the role of frontostriatal limbic loop hypoconnectivity in early-stage schizophrenia pathology and suggest potential novel therapeutic targets.

Brain sexual differentiation and effects of cross-sex hormone therapy in transpeople: A resting-state functional magnetic resonance study

It is hypothesized that transpeople show sex-atypical differentiation of the brain. Various structural neuroimaging studies provide support for this notion, but little is known about the sexual differentiation of functional resting-state networks in transpeople. In this study we therefore aimed to determine whether brain functional connectivity (FC) patterns in transpeople are sex-typical or sex-atypical, before and after the start of cross-sex hormone therapy (CHT). We acquired resting-state functional magnetic resonance data in 36 transpeople (22 with female sex assigned at birth), first during gonadal suppression, and again four months after start of CHT, and in 37 cisgender people (20 females), both sessions without any hormonal intervention. We used independent component analysis to identify the default mode network (DMN), salience network (SN), and left and right working memory network (WMN). These spatial maps were used for group comparisons. Within the DMN, SN, and left WMN similar FC patterns were found across groups. However, within the right WMN, cisgender males showed significantly greater FC in the right caudate nucleus than cisgender females. There was no such sex difference in FC among the transgender groups and they did not differ significantly from either of the cisgender groups. CHT (in transgender participants) and circulating sex steroids (in cisgender participants) did not affect FC. Our findings may suggest that cisgender males and females experience a dissimilar (early) differentiation of the right WMN and that such differentiation is less pronounced in transpeople.

Abnormal neural activities of directional brain networks in patients with long-term bilateral hearing loss.

The objective of the study is to provide some implications for rehabilitation of hearing impairment by investigating changes of neural activities of directional brain networks in patients with long-term bilateral hearing loss. Firstly, we implemented neuropsychological tests of 21 subjects (11 patients with long-term bilateral hearing loss, and 10 subjects with normal hearing), and these tests revealed significant differences between the deaf group and the controls. Then we constructed the individual specific virtual brain based on functional magnetic resonance data of participants by utilizing effective connectivity and multivariate regression methods. We exerted the stimulating signal to the primary auditory cortices of the virtual brain and observed the brain region activations. We found that patients with long-term bilateral hearing loss presented weaker brain region activations in the auditory and language networks, but enhanced neural activities in the default mode network as compared with normally hearing subjects. Especially, the right cerebral hemisphere presented more changes than the left. Additionally, weaker neural activities in the primary auditor cortices were also strongly associated with poorer cognitive performance. Finally, causal analysis revealed several interactional circuits among activated brain regions, and these interregional causal interactions implied that abnormal neural activities of the directional brain networks in the deaf patients impacted cognitive function.

Functional brain segmentation using inter-subject correlation in fMRI.

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily-life situations. A new exploratory data-analysis approach, functional segmentation inter-subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable. FuSeISC was tested using fMRI data sets collected during traditional block-design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only useful for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but also has other potential applications, such as generation of a functional brain atlases including both lower- and higher-order processing areas. Finally, as a part of FuSeISC, a criterion-based sparsification of the shared nearest-neighbor graph was proposed for detecting clusters in noisy data. In the tests with synthetic data, this technique was superior to well-known clustering methods, such as Ward's method, affinity propagation, and K-means ++. Hum Brain Mapp 38:2643-2665, 2017. © 2017 Wiley Periodicals, Inc.

Encoding, preparation and implementation of novel complex verbal instructions

Verbal instructions allow humans to acquire and implement complex novel rules in few seconds. A major question that remains elusive is how the brain represents this information prior to successful task execution. In this experiment, we studied the brain regions involved in representing categorical stimulus information during the encoding of novel instructions, their preparation and also their implementation, as well as the relation of the fidelity of these representations to observable behavior. To do so, we devised a novel instructions paradigm to delimitate these three stages. Using univariate and multivariate analyses of functional magnetic resonance data, our study revealed that the semantic content (faces or letters) of complex novel instructions can be decoded several seconds before the onset of a target, as soon as instructions are encoded. Crucially, the quality of the information represented in domain-general and category-selective regions correlated with subsequent behavioral performance. This suggests that the rapid transformation of novel instructions into coherent behavior is supported by control mechanisms that use available, relevant information about the current rule prior to its execution. In addition, our results highlight the relation between these control processes and others such as prospective memory and maintenance of future intentions.

Primary visual cortical remapping in patients with inherited peripheral retinal degeneration.

Human studies addressing the long-term effects of peripheral retinal degeneration on visual cortical function and structure are scarce. Here we investigated this question in patients with Retinitis Pigmentosa (RP), a genetic condition leading to peripheral visual degeneration. We acquired functional and anatomical magnetic resonance data from thirteen patients with different levels of visual loss and twenty-two healthy participants to study primary (V1) visual cortical retinotopic remapping and cortical thickness. We identified systematic visual field remapping in the absence of structural changes in the primary visual cortex of RP patients. Remapping consisted in a retinotopic eccentricity shift of central retinal inputs to more peripheral locations in V1. Importantly, this was associated with changes in visual experience, as assessed by the extent of the visual loss, with more constricted visual fields resulting in larger remapping. This pattern of remapping is consistent with expansion or shifting of neuronal receptive fields into the cortical regions with reduced retinal input. These data provide evidence for functional changes in V1 that are dependent on the magnitude of peripheral visual loss in RP, which may be explained by rapid cortical adaptation mechanisms or long-term cortical reorganization. This study highlights the importance of analyzing the retinal determinants of brain functional and structural alterations for future visual restoration approaches.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave.

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license.Website: http://cosmomvpa.orgSource code: https://github.com/CoSMoMVPA/CoSMoMVPA

FMRI goes individual.

Functional magnetic resonance data are traditionally analyzed on a population level, but new work shows that meaningful information can be extracted from individual subjects.

Brain default mode network study of consciousness disorder patients with diffuse axonal injury

Objective To investigate the early abnormal functional connectivity of brain default mode network (DMN) and its evaluation for consciousness level after injury in consciousness disorder patients with diffuse axonal injury. Methods A total of 15 patients with consciousness disorder diagnosed as diffuse axonal injury at Department of Neurosurgery, Wuhan General Hospital of Guangzhou Military Command from April 2013 to March 2014 were selected. The resting-state functional magnetic resonance data of the 15 consciousness disorder patients with diffuse axonal injury and 15 healthy volunteers were collected. The independent component analysis was used to extract the DMN ingredient in each subject. The two-sample t-test was performed between the groups in order to obtain the different brain areas, and the correlation analysis of the Glasgow coma scale (GCS) and the coma recovery scale-revised (CRS-R) at 6 months after injury were performed at the different brain areas and the magnetic resonance data were collected on the same day. Results The functional connectivity degree of the precuneus/posterior cingulate, bilateral inferior parietal lobule, ventromedial prefrontal cortex, and left temporal lobe in brain DMN in patients with diffuse axonal injury were significantly lower than those of control group (P<0.01). The functional connectivity of the precuneus/posterior cingulated and GCS showed significant power correlation (P<0.05), and it also showed significant power correlation with CRS-R (P<0.01). Conclusions The early brain DMN functional connectivity has abnormality in consciousness disorder patients with diffuse axonal injury. The abnormal functional connectivity of the precuneus/posterior cingulate may become an indicator for assessing the degree of consciousness disorder and the prognosis of consciousness disorder. Key words: Diffuse axonal injury; Consciousness disorders; Prognosis; Magnetic resonance imaging; Default mode network

Identifying Cognitive States Using Regularity Partitions.

Functional Magnetic Resonance (fMRI) data can be used to depict functional connectivity of the brain. Standard techniques have been developed to construct brain networks from this data; typically nodes are considered as voxels or sets of voxels with weighted edges between them representing measures of correlation. Identifying cognitive states based on fMRI data is connected with recording voxel activity over a certain time interval. Using this information, network and machine learning techniques can be applied to discriminate the cognitive states of the subjects by exploring different features of data. In this work we wish to describe and understand the organization of brain connectivity networks under cognitive tasks. In particular, we use a regularity partitioning algorithm that finds clusters of vertices such that they all behave with each other almost like random bipartite graphs. Based on the random approximation of the graph, we calculate a lower bound on the number of triangles as well as the expectation of the distribution of the edges in each subject and state. We investigate the results by comparing them to the state of the art algorithms for exploring connectivity and we argue that during epochs that the subject is exposed to stimulus, the inspected part of the brain is organized in an efficient way that enables enhanced functionality.

GPU-based parallel group ICA for functional magnetic resonance data

The goal of our study is to develop a fast parallel implementation of group independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data using graphics processing units (GPU). Though ICA has become a standard method to identify brain functional connectivity of the fMRI data, it is computationally intensive, especially has a huge cost for the group data analysis. GPU with higher parallel computation power and lower cost are used for general purpose computing, which could contribute to fMRI data analysis significantly. In this study, a parallel group ICA (PGICA) on GPU, mainly consisting of GPU-based PCA using SVD and Infomax-ICA, is presented. In comparison to the serial group ICA, the proposed method demonstrated both significant speedup with 6–11 times and comparable accuracy of functional networks in our experiments. This proposed method is expected to perform the real-time post-processing for fMRI data analysis.

Point process analysis in brain networks of patients with diabetes

Noise and individual differences arise from disturbances in the effective use of resting-state functional magnetic resonance image (fMRI) datasets. In this study, the point process is used to treat fMRI datasets of healthy controls and patients with diabetes; then, functional brain networks of subjects are established using two sets of BOLD signals. The results illustrate that differences between the healthy controls and the patients were more obvious in point process signals than nonpoint process signals. Our results also suggest that there is a higher recognition accuracy of the signals by preprocessing with the point process. These findings may suggest that the point process approach can reduce BOLD signals noise, providing a new method for functional magnetic resonance data preprocessing, and may provide a promising method for early data preprocessing in computer-aided disease diagnostics.

Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep.

The mining of huge databases of resting-state brain activity recordings represents state of the art in the assessment of endogenous neuronal activity-and may be a promising tool in the search for functional biomarkers. However, the resting state is an uncontrolled condition and its heterogeneity is neither sufficiently understood nor accounted for. We test the hypothesis that subjects exhibit unstable wakefulness, i.e., drift into sleep during typical resting-state experiments. Analyzing 1,147 resting-state functional magnetic resonance data sets, we revealed a reliable loss of wakefulness in a third of subjects within 3 min and demonstrated the dynamic nature of the resting state, with fundamental changes in the associated functional neuroanatomy. Implications include the necessity of wakefulness monitoring and modeling, taking measures to maintain a state of wakefulness, acknowledging the possibility of sleep and exploring its consequences, and especially the critical assessment of possible false-positive or false-negative results.

Measuring brain temperature without a thermometer.

Temperature has profound effects on a wide range of parameters of neural activity at various scales (Hodgkin and Katz, 1949). At the cell level, ionic currents, membrane potential, input resistance, action potential amplitude, duration and propagation, and synaptic transmission have all been shown to be affected by temperature variations (Hodgkin and Katz, 1949; Kullmann and Asztely, 1998; Volgushev et al., 2000a,b; Fujii et al., 2002). At mesoscopic scales of neural activity, temperature changes can steer network activity toward different functional regimes (Reig et al., 2010), affecting the duration, frequency and firing rate of activated states during slow frequency oscillations, and the ability to end these states (Compte et al., 2003). Temperature also has a substantial effect on chemical reaction rates (Swan, 1974), and affects the blood oxygen saturation level by changing haemoglobin affinity for oxygen (Guyton, 1987). Furthermore, cooling reduces metabolic processes (Esmann and Skou, 1988), and has been used to silence cortical areas to study their function (Uyeda and Fuster, 1967). While from single cell to mesoscopic levels temperature can directly be measured, at the system level of non-invasive studies using electroencephalogram or functional magnetic resonance, it can only be estimated indirectly, using the temperature dependence of the magnetic resonance signal's frequency (Hindman, 1966; Parker et al., 1983; Kuroda et al., 1996). Furthermore, a theoretical model of brain temperature (Yablonskiy et al., 2000; Sukstankii and Yablonskiy, 2006) allows inferring from functional magnetic resonance data that functional stimulation can induce local brain temperature fluctuations of up to ±1°C with respect to resting temperature, by locally changing the balance between metabolic heat production and heat removal by blood flow. The potential impact of temperature modulations on functional brain activity is significant. Given a temperature effect on blood oxygen saturation levels of several percent/1°C (Guyton, 1987), and an estimated average brain van't Hoff temperature coefficient Q10 (the factor by which a reaction rate increases for 10°C increases) of 2,3 (Swan, 1974), the observed temperature fluctuations may lead to sizeable changes in blood oxygen saturation levels and to >2% variations in chemical reaction rates. Here we propose a way to directly quantify temperature from system-level brain recordings, and show how it can be used to characterize neural activity associated with cognitive function.