Toward a Neurocognitive Understanding of the Algorithms That Underlie Metamemory Judgments

2020 ◽  
Vol 228 (4) ◽  
pp. 233-243 ◽  
Author(s):  
Timothy Kelley ◽  
Michael J. Serra ◽  
Tyler Davis

Abstract. Neurocognitive research on metamemory thus far has mostly focused on localizing brain regions that track metacognitive judgments and distinguishing metacognitive processing from primary cognition. With much known about the localization of metamemory in the brain, there is a growing opportunity to develop a more algorithmic characterization of the brain processes underlying metamemory. We briefly review some current neurocognitive metamemory research, including relevant brain regions and theories about their role in metamemory. We review some computational neuroimaging approaches and, as an illustrative example, describe their use in studies on the delayed-JOL (judgments of learning) effect. Finally, we discuss how researchers might apply computational approaches to several unresolved questions in the behavioral metamemory literature. Such research could provide a bridge between cognitive and neurocognitive research on metamemory and provide novel insights into the algorithms underlying metamemory judgments, thus informing theory and methodology in both areas.

2021 ◽  
Vol 15 ◽  
Author(s):  
Paolo Finotelli ◽  
Carlo Piccardi ◽  
Edie Miglio ◽  
Paolo Dulio

In this paper, we propose a graphlet-based topological algorithm for the investigation of the brain network at resting state (RS). To this aim, we model the brain as a graph, where (labeled) nodes correspond to specific cerebral areas and links are weighted connections determined by the intensity of the functional magnetic resonance imaging (fMRI). Then, we select a number of working graphlets, namely, connected and non-isomorphic induced subgraphs. We compute, for each labeled node, its Graphlet Degree Vector (GDV), which allows us to associate a GDV matrix to each one of the 133 subjects of the considered sample, reporting how many times each node of the atlas “touches” the independent orbits defined by the graphlet set. We focus on the 56 independent columns (i.e., non-redundant orbits) of the GDV matrices. By aggregating their count all over the 133 subjects and then by sorting each column independently, we obtain a sorted node table, whose top-level entries highlight the nodes (i.e., brain regions) most frequently touching each of the 56 independent graphlet orbits. Then, by pairwise comparing the columns of the sorted node table in the top-k entries for various values of k, we identify sets of nodes that are consistently involved with high frequency in the 56 independent graphlet orbits all over the 133 subjects. It turns out that these sets consist of labeled nodes directly belonging to the default mode network (DMN) or strongly interacting with it at the RS, indicating that graphlet analysis provides a viable tool for the topological characterization of such brain regions. We finally provide a validation of the graphlet approach by testing its power in catching network differences. To this aim, we encode in a Graphlet Correlation Matrix (GCM) the network information associated with each subject then construct a subject-to-subject Graphlet Correlation Distance (GCD) matrix based on the Euclidean distances between all possible pairs of GCM. The analysis of the clusters induced by the GCD matrix shows a clear separation of the subjects in two groups, whose relationship with the subject characteristics is investigated.


2017 ◽  
Author(s):  
J. M. Schoffelen ◽  
A. Hultén ◽  
N. Lam ◽  
A. Marquand ◽  
J. Uddén ◽  
...  

AbstractThe brain’s remarkable capacity for language requires bidirectional interactions between functionally specialized brain regions. We used magnetoencephalography to investigate interregional interactions in the brain network for language, while 102 participants were reading sentences. Using Granger causality analysis, we identified inferior frontal cortex and anterior temporal regions to receive widespread input, and middle temporal regions to send widespread output. This fits well with the notion that these regions play a central role in language processing. Characterization of the functional topology of this network, using data-driven matrix factorization, which allowed for partitioning into a set of subnetworks, revealed directed connections at distinct frequencies of interaction. Connections originating from temporal regions peaked at alpha frequency, whereas connections originating from frontal and parietal regions peaked at beta frequency. These findings indicate that processing different types of linguistic information may depend on the contributions of distinct brain rhythms.One Sentence SummaryCommunication between language relevant areas in the brain is supported by rhythmic synchronization, where different rhythms reflect the direction of information flow.


2019 ◽  
Author(s):  
Alican Nalci ◽  
Wenjing Luo ◽  
Thomas T. Liu

AbstractIn resting-state functional MRI, the correlation between blood-oxygenation-level-dependent (BOLD) signals across brain regions is used to estimate the functional connectivity (FC) of the brain. FC estimates are prone to the influence of nuisance factors including scanner-related artifacts and physiological modulations of the BOLD signal. Nuisance regression is widely performed to reduce the effect of nuisance factors on FC estimates on a per-scan basis. However, a dedicated analysis of nuisance effects on the variability of FC metrics across a collection of scans has been lacking. This work investigates the effects of nuisance factors on the variability of FC estimates across a collection of scans both before and after nuisance regression. Inter-scan variations in FC estimates are shown to be significantly correlated with the geometric norms of various nuisance terms, including head motion measurements, signals derived from white-matter and cerebrospinal regions, and the whole-brain global signal (GS) both before and after nuisance regression. In addition, it is shown that GS regression (GSR) can introduce GS norm-related fluctuations that are negatively correlated with inter-scan FC estimates. The empirical results are shown to be largely consistent with the predictions of a theoretical framework previously developed for the characterization of dynamic FC measures. This work shows that caution must be exercised when interpreting inter-scan FC measures across scans both before and after nuisance regression.


2020 ◽  
Author(s):  
Ayan S. Mandal ◽  
Rafael Romero-Garcia ◽  
Michael G. Hart ◽  
John Suckling

AbstractA better understanding of the nonrandom localization patterns of gliomas across the brain could lend clues to the origins of these types of tumors. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumor frequency map. Using this map, we demonstrated that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with stem-like cells also exhibited a high glioma frequency. Furthermore, gliomas were localized to brain regions enriched with the expression of genes associated with chromatin organization and synaptic signaling. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of oncogenesis.


2022 ◽  
Author(s):  
Zhong Xiaoling ◽  
Li Feng ◽  
Tan Guiyuan ◽  
Yi Li ◽  
Zhao Jiaxin ◽  
...  

Brain is the most complex organ of living organisms, as the celebrated cells in the brain, microglia play an indispensable role in the brain's immune microenvironment. Microglia have critical roles not only in neural development and homeostasis, but also in neurodegenerative diseases and malignant of the central nervous system. However, little is known about the dynamic characteristics of microglia during development or disease conditions. Recently, the single-cell RNA sequencing technologies have become possible to characterize the heterogeneity of immune system in brain. But it posed computational challenges on integrating and utilizing the massive published datasets to dissect the spatiotemporal characterization of microglia. Here, we present microgliaST (bio-bigdata.hrbmu.edu.cn/MST), a database consisting of single-cell microglia transcriptomes across multiple brain regions and developmental periods. Based on high-quality microglia markers collected from published papers, we annotated and constructed human and mouse transcriptomic profiles of 273,374 microglias, comprising 12 regions, 12 periods and 3 conditions (normal, disease, treatment). In addition, MicrogliaST provides multiple analytical tools to elucidate the landscape of microglia under disorder conditions, conduct personalized difference analysis and spatiotemporal dynamic analysis. More importantly, microgliaST paves an ingenious way to the study of brain environment, and also provides insights into clinical therapy assessments.


2021 ◽  
Author(s):  
Mitsuru Shinohara ◽  
Junko Hirokawa ◽  
Akemi Shimodaira ◽  
Yoshitaka Tashiro ◽  
Kaoru Suzuki ◽  
...  

Abstract Background: One main pathological hallmark of Alzheimer’s disease (AD) is tau accumulation as neurofibrillary tangles (NFTs) in the brain. Although sandwich enzyme-linked immunosorbent assays (ELISAs) are useful for quantifying tau levels, including those in CSF, plasma and brain, it has not yet been determined which antibody combination is the most appropriate for assessing the neuropathological accumulation of tau in the brain. Methods: We developed several sandwich tau ELISAs by introducing antibodies against several tau epitopes, including from its N-terminal and C-terminal regions, and evaluated tau levels depending on disease stage, brain areas, and other AD-related changes. Results: We observed that tau levels in insoluble brain fraction determined by each ELISAs differ depending on the epitopes of the antibodies: there is a trend that non-AD control samples yield relatively high signals when an antibody against the N-terminal region of tau is used. On the other hand, ELISAs combining two antibodies against the later-middle to C-terminal regions of tau produced substantially increased signals from AD samples, compared to those from non-AD controls. Such ELISAs better distinguish AD and non-AD controls, and the results are more closely associated with Braak NFT stage, Aβ accumulation, and neuroinflammatory markers. In addition, these ELISAs can reflect the pattern of tau spread across brain regions. Conclusions: Tau ELISAs that combine two antibodies against the later-middle to C-terminal regions of tau can better reflect neuropathological tau accumulation, which would enable to evaluate tau accumulation in the brain at a biochemical level.


2019 ◽  
Author(s):  
Adam Kimbrough ◽  
Lauren C. Smith ◽  
Marsida Kallupi ◽  
Sierra Simpson ◽  
Andres Collazo ◽  
...  

AbstractNumerous brain regions have been identified as contributing to addiction-like behaviors, but unclear is the way in which these brain regions as a whole lead to addiction. The search for a final common brain pathway that is involved in addiction remains elusive. To address this question, we used male C57BL/6J mice and performed single-cell whole-brain imaging of neural activity during withdrawal from cocaine, methamphetamine, and nicotine. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical- to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway that leads to addiction.Significance StatementA key aspect of treating drug abuse is understanding similarities and differences of how drugs of abuse affect the brain. In the present study we examined how the brain is altered during withdrawal from psychostimulants. We found that each drug produced a unique pattern of activity in the brain, but that brains in withdrawal from cocaine and methamphetamine shared similar features. Interestingly, we found the major common link between withdrawal from all psychostimulants, when compared to controls, was a shift in the broad organization of the brain in the form of reduced modularity. Reduced modularity has been shown in several brain disorders, including traumatic brain injury, and dementia, and may be the common link between drugs of abuse.


Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.


2020 ◽  
Vol 21 ◽  
Author(s):  
Sayed Md Mumtaz ◽  
Gautam Bhardwaj ◽  
Shikha Goswami ◽  
Rajiv Kumar Tonk ◽  
Ramesh K. Goyal ◽  
...  

: The Glioblastoma Multiforme (GBM; grade IV astrocytoma) exhort tumor of star-shaped glial cell in the brain. It is a fast-growing tumor that spreads to nearby brain regions specifically to cerebral hemispheres in frontal and temporal lobes. The etiology of GBM is unknown, but major risk factors are genetic disorder like neurofibromatosis and schwanomatosis which develop the tumor in the nervous system. The management of GBM with chemo-radio therapy leads to resistance and current drug regimen like Temozolomide (TMZ) is less efficacious. The reasons behind failure of drugs are due to DNA alkylation in cell cycle by enzyme DNA guanidase and mitochondrial dysfunction. Naturally occurring bio-active compounds from plants known as phytochemicals, serve as vital sources for anti-cancer drugs. Some typical examples include taxol analogs, vinca alkaloids such as vincristine, vinblastine, podophyllotoxin analogs, camptothecin, curcumin, aloe emodin, quercetin, berberine e.t.c. These phytochemicals often act via regulating molecular pathways which are implicated in growth and progression of cancers. However the challenges posed by the presence of BBB/BBTB to restrict passage of these phytochemicals, culminates in their low bioavailability and relative toxicity. In this review we integrated nanotech as novel drug delivery system to deliver phytochemicals from traditional medicine to the specific site within the brain for the management of GBM.


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