scholarly journals Temporal pattern recognition in the human brain: a dual simultaneous processing

2021 ◽  
Author(s):  
Leonardo Bonetti ◽  
Elvira Brattico ◽  
Silvia EP Bruzzone ◽  
Giulia Donati ◽  
Gustavo Deco ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Human Brain ◽  
Temporal Pattern ◽  
Brain Regions ◽  
Machine Learning Algorithms ◽  
Brain Areas ◽  
Simultaneous Processing ◽  
Neural Information ◽  
Complex Procedure ◽  
Magnetic Resonance Imaging Mri

Pattern recognition is a major scientific topic. Strikingly, while machine learning algorithms are constantly refined, the human brain emerges as an ancestral biological example of such complex procedure. However, how it transforms sequences of single objects into meaningful temporal patterns remains elusive. Using magnetoencephalography (MEG) and magnetic resonance imaging (MRI), we discovered and mathematically modelled an inedited dual simultaneous processing responsible for pattern recognition in the brain. Indeed, while the objects of the temporal pattern were independently elaborated by a local, rapid brain processing, their combination into a meaningful superordinate pattern depended on a concurrent global, slower processing involving a widespread network of sequentially active brain areas. Expanding the established knowledge of neural information flow from low- to high-order brain areas, we revealed a novel brain mechanism based on simultaneous activity in different frequency bands within the same brain regions, highlighting its crucial role underlying complex cognitive functions.

scholarly journals Taste quality representation in the human brain

2019 ◽  
Author(s):  
Jason A. Avery ◽  
Alexander G. Liu ◽  
John E. Ingeholm ◽  
Cameron D. Riddell ◽  
Stephen J. Gotts ◽  
...  
Keyword(s):  
Human Brain ◽  
Cortical Neurons ◽  
Brain Regions ◽  
Taste Quality ◽  
7 Tesla ◽  
Mammalian Brain ◽  
High Magnetic Field Strength ◽  
Magnetic Resonance Imaging Mri ◽  
Spatial Locations ◽  
Univariate Analyses

SUMMARYIn the mammalian brain, the insula is the primary cortical substrate involved in the perception of taste. Recent imaging studies in rodents have identified a gustotopic organization in the insula, whereby distinct insula regions are selectively responsive to one of the five basic tastes. However, numerous studies in monkeys have reported that gustatory cortical neurons are broadly-tuned to multiple tastes, and tastes are not represented in discrete spatial locations. Neuroimaging studies in humans have thus far been unable to discern between these two models, though this may be due to the relatively low spatial resolution employed in taste studies to date. In the present study, we examined the spatial representation of taste within the human brain using ultra-high resolution functional magnetic resonance imaging (MRI) at high magnetic field strength (7-Tesla). During scanning, participants tasted sweet, salty, sour and tasteless liquids, delivered via a custom-built MRI-compatible tastant-delivery system. Our univariate analyses revealed that all tastes (vs. tasteless) activated primary taste cortex within the bilateral dorsal mid-insula, but no brain region exhibited a consistent preference for any individual taste. However, our multivariate searchlight analyses were able to reliably decode the identity of distinct tastes within those mid-insula regions, as well as brain regions involved in affect and reward, such as the striatum, orbitofrontal cortex, and amygdala. These results suggest that taste quality is not represented topographically, but by a combinatorial spatial code, both within primary taste cortex as well as regions involved in processing the hedonic and aversive properties of taste.

The Double Take of Expertise: Neural Expansion Is Associated With Outstanding Performance

2018 ◽  
Vol 27 (6) ◽  
pp. 462-469 ◽  
Author(s):  
Merim Bilalić
Keyword(s):  
Pattern Recognition ◽  
Human Brain ◽  
Brain Activation ◽  
Cognitive System ◽  
Brain Areas ◽  
Opposite Hemisphere ◽  
Computational Burden ◽  
Complex Skills ◽  
Neural Efficiency ◽  
The Brain

The performance of experts seems almost effortless. The neural-efficiency hypothesis takes this into account, suggesting that because of practice and automatization of procedures, experts require fewer brain resources. Here, I argue that the way the brain accommodates complex skills does indeed have to do with the nature of experts’ performance. However, instead of exhibiting less brain activation, experts’ performance actually engages more brain areas. Behind the seemingly effortless performance of experts lies a complex cognitive system that relies on knowledge about the domain of expertise. Unlike novices, who need to execute one process at a time, experts are able to recognize an object, retrieve its function, and connect it to another object simultaneously. The expert brain deals with this computational burden by engaging not only specific brain areas in one hemisphere but also the same (homologous) area in the opposite hemisphere. This phenomenon, which I call the double take of expertise, has been observed in a number of expertise domains. I describe it here in object- and pattern-recognition tasks in the domain of chess. I also discuss the importance of the study of expertise for our understanding of the human brain in general.

scholarly journals Global Signal Topography of the Human Brain: A Novel Framework of Functional Connectivity for Psychological and Pathological Investigations

2021 ◽  
Vol 15 ◽  
Author(s):  
Yujia Ao ◽  
Yujie Ouyang ◽  
Chengxiao Yang ◽  
Yifeng Wang
Keyword(s):  
Human Brain ◽  
Spatial Information ◽  
Brain Regions ◽  
Intrinsic Structure ◽  
Global Signal ◽  
Neural Information ◽  
Sensory Cortices ◽  
Brain States ◽  
Global And Local ◽  
Local Brain

The global signal (GS), which was once regarded as a nuisance of functional magnetic resonance imaging, has been proven to convey valuable neural information. This raised the following question: what is a GS represented in local brain regions? In order to answer this question, the GS topography was developed to measure the correlation between global and local signals. It was observed that the GS topography has an intrinsic structure characterized by higher GS correlation in sensory cortices and lower GS correlation in higher-order cortices. The GS topography could be modulated by individual factors, attention-demanding tasks, and conscious states. Furthermore, abnormal GS topography has been uncovered in patients with schizophrenia, major depressive disorder, bipolar disorder, and epilepsy. These findings provide a novel insight into understanding how the GS and local brain signals coactivate to organize information in the human brain under various brain states. Future directions were further discussed, including the local-global confusion embedded in the GS correlation, the integration of spatial information conveyed by the GS, and temporal information recruited by the connection analysis. Overall, a unified psychopathological framework is needed for understanding the GS topography.

scholarly journals Localizing Epileptogenic Zone from High Density EEG Data Using Machine Learning

2021 ◽  
Vol 17 (06) ◽  
pp. 73
Author(s):  
Sehresh Khan ◽  
Aunsia Khan ◽  
Nazia Hameed ◽  
Muhammad Aleem Taufiq ◽  
Saba Riaz
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Antiepileptic Drugs ◽  
Focal Epilepsy ◽  
Brain Regions ◽  
High Density ◽  
Machine Learning Algorithms ◽  
Epileptogenic Zone ◽  
Drug Resistant ◽  
Time Frequency

<span>Drug-resistant focal epilepsy is the failure of antiepileptic drugs scheduled to obtain epileptic free brain activities. In human brain, cerebral hemispheres are the most commonly involved brain regions in epilepsy. In case of antiepileptic drugs failure, surgical treatment is the best cure possible. However, correct localization of epileptogenic region is a challenging task for neurologists, while for computer scientists, automatic localization is. This research work’s aim is to explore the functional activities of all brain regions in drug-resistant focal epileptic patients and achieve high accuracy for the classification of epileptogenic region (ER) with the high-density electroencephalographic (hdEEG) data. The proposed system includes frequency analysis for feature extractions followed by individual subject’s registration of hdEEG signals with anatomical brain images for most precise localization of ER possible. The datasets attained from feature extraction process are then preprocessed for class imbalanced and then evaluated using different machine learning algorithms including the techniques under Bayesian networks, Lazy networks, Meta techniques, Rule based systems and Tree structured algorithms. Considering human brain as stationary object as well as dynamic object, frequency-based and time-frequency based features were considered in 12 subjects respectively. Through this novel approach, 99.70% accuracy is achieved to classify ER from healthy regions using KSTAR and using IBK algorithm, 91.60% accuracy has been achieved to classify generator from propagator.</span>

scholarly journals The Brain Is Globally Symmetric: An Analysis of Intra and Interhemispheric Symmetry of Subcortical Structures in the Aging Human Brain

2021 ◽  
Author(s):  
Jaime Gomez Ramirez ◽  
Javier J González-Rosa
Keyword(s):  
Human Brain ◽  
Resonance Imaging ◽  
Subcortical Structures ◽  
Brain Areas ◽  
Elderly Individuals ◽  
Statistical Dependency ◽  
Magnetic Resonance Imaging Mri ◽  
Effects Of Aging ◽  
The Brain ◽  
Adjacent Brain

Abstract Here we address the hemispheric interdependency of subcortical structures in the aging human brain. In particular, we investigate whether volume variation can be explained with the adjacency of structures in the same hemisphere or is due to the interhemispheric development of mirror subcortical structures in the brain. Seven subcortical structures in both hemispheres were automatically segmented in a large sample of over three 3,312 magnetic resonance imaging (MRI) studies of elderly individuals in their 70s and 80s. We perform Eigenvalue analysis to find that anatomic volumes in the limbic system and basal ganglia show similar statistical dependency when considered in the same hemisphere (intrahemispheric) or in different hemispheres (interhemispheric). Our results indicate that anatomic bilaterality is preserved in the aging human brain, supporting the hypothesis that coupling between non-adjacent brain areas could act as a mechanism to compensate for the deleterious effects of aging.

scholarly journals Switch independent task representations in frontal and parietal cortex

2017 ◽  
Author(s):  
Lasse S. Loose ◽  
David Wisniewski ◽  
Marco Rusconi ◽  
Thomas Goschke ◽  
John-Dylan Haynes
Keyword(s):  
Human Brain ◽  
Parietal Cortex ◽  
Task Switching ◽  
Brain Regions ◽  
Task Representation ◽  
Related Information ◽  
Time To Build ◽  
Additional Control ◽  
Neural Information ◽  
Task Representations

AbstractAlternating between two tasks is effortful and impairs performance. Previous functional magnetic resonance imaging (fMRI) studies have found increased activity in fronto-parietal cortex when task switching is required. One possibility is that the additional control demands for switch trials are met by strengthening task representations in the human brain. Alternatively, on switch trials the residual representation of the previous task might impede the buildup of a neural task representation. This would predict weaker task representations on switch trials, thus also explaining the performance costs. To test this, participants were cued to perform one of two similar tasks, with the task being repeated or switched between successive trials. MVPA was used to test which regions encode the tasks and whether this encoding differs between switch and repeat trials. As expected, we found information about task representations in frontal and parietal cortex, but there was no difference in the decoding accuracy of task-related information between switch and repeat trials. Using cross-classification we found that the fronto-parietal cortex encodes tasks using a similar spatial pattern in switch and repeat trials. Thus, task representations in frontal and parietal cortex are largely switch-independent. We found no evidence that neural information about task representations in these regions can explain behavioral costs usually associated with task switching.Significance statementAlternating between two tasks is effortful and slows down performance. One possible explanation is that the representations in the human brain need time to build up and are thus weaker on switch trials, explaining performance costs. Alternatively, task representations might even be enhanced in order to overcome the previous task. Here we used a combination of fMRI and a brain classifier to test whether the additional control demands under switching conditions lead to an increased or decreased strength of task representations in fronto-parietal brain regions. We found that task representations are not significantly modulated by switching processes. Thus, task representations in the human brain cannot account for the performance costs associated with alternating between tasks.

Engaging regularly with fiction influences connectivity in cortical areas for language and mentalizing

2017 ◽  
Author(s):  
Roel M. Willems ◽  
Franziska Hartung
Keyword(s):  
Functional Connectivity ◽  
Time Course ◽  
Language Skills ◽  
Brain Regions ◽  
Brain Areas ◽  
Daily Lives ◽  
Cortical Areas ◽  
Social Abilities ◽  
Behavioral Evidence ◽  
Amount Of Reading

Behavioral evidence suggests that engaging with fiction is positively correlated with social abilities. The rationale behind this link is that engaging with fictional narratives offers a ‘training modus’ for mentalizing and empathizing. We investigated the influence of the amount of reading that participants report doing in their daily lives, on connections between brain areas while they listened to literary narratives. Participants (N=57) listened to two literary narratives while brain activation was measured with fMRI. We computed time-course correlations between brain regions, and compared the correlation values from listening to narratives to listening to reversed speech. The between-region correlations were then related to the amount of fiction that participants read in their daily lives. Our results show that amount of fiction reading is related to functional connectivity in areas known to be involved in language and mentalizing. This suggests that reading fiction influences social cognition as well as language skills.

scholarly journals Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 642
Author(s):  
Yi-Da Wu ◽  
Ruey-Kai Sheu ◽  
Chih-Wei Chung ◽  
Yen-Ching Wu ◽  
Chiao-Chi Ou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Excellent Agreement ◽  
Antinuclear Antibody ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Observer Agreement ◽  
Supervised Machine Learning ◽  
International Consensus ◽  
Testing Data

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

scholarly journals Alcohol use disorder causes global changes in splicing in the human brain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Derek Van Booven ◽  
Mengying Li ◽  
J. Sunil Rao ◽  
Ilya O. Blokhin ◽  
R. Dayne Mayfield ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Human Brain ◽  
Alcohol Use Disorder ◽  
Brain Regions ◽  
Gene Set Enrichment Analysis ◽  
Splicing Factor ◽  
Central Nucleus ◽  
Global Changes ◽  
Aberrant Expression ◽  
Altered Expression

AbstractAlcohol use disorder (AUD) is a widespread disease leading to the deterioration of cognitive and other functions. Mechanisms by which alcohol affects the brain are not fully elucidated. Splicing constitutes a nuclear process of RNA maturation, which results in the formation of the transcriptome. We tested the hypothesis as to whether AUD impairs splicing in the superior frontal cortex (SFC), nucleus accumbens (NA), basolateral amygdala (BLA), and central nucleus of the amygdala (CNA). To evaluate splicing, bam files from STAR alignments were indexed with samtools for use by rMATS software. Computational analysis of affected pathways was performed using Gene Ontology Consortium, Gene Set Enrichment Analysis, and LncRNA Ontology databases. Surprisingly, AUD was associated with limited changes in the transcriptome: expression of 23 genes was altered in SFC, 14 in NA, 102 in BLA, and 57 in CNA. However, strikingly, mis-splicing in AUD was profound: 1421 mis-splicing events were detected in SFC, 394 in NA, 1317 in BLA, and 469 in CNA. To determine the mechanism of mis-splicing, we analyzed the elements of the spliceosome: small nuclear RNAs (snRNAs) and splicing factors. While snRNAs were not affected by alcohol, expression of splicing factor heat shock protein family A (Hsp70) member 6 (HSPA6) was drastically increased in SFC, BLA, and CNA. Also, AUD was accompanied by aberrant expression of long noncoding RNAs (lncRNAs) related to splicing. In summary, alcohol is associated with genome-wide changes in splicing in multiple human brain regions, likely due to dysregulation of splicing factor(s) and/or altered expression of splicing-related lncRNAs.

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