Individual Brain Areas Research Articles (Page 1)

Objective.Computational modeling has recently emerged as a powerful tool to better understand seizure dynamics and guide new treatment strategies. This work aims to develop and personalize whole-brain computational models in epilepsy using multimodal clinical data to simulate and evaluate individualized therapeutic strategies.Approach.We present a computational framework that constructs patient-specific whole-brain models of seizure propagation by integrating SEEG, MRI, and diffusion MRI data. The pipeline uses neural mass models for each node in the network, simulating whole-brain dynamics. Model personalization involves adjusting global and local parameters representing the excitability of individual brain areas, using an evolutionary algorithm that aims to maximize the correlation between empirical and synthetic functional connectivity matrices derived from SEEG data.Main results.The resulting personalized models successfully reproduce individual seizure propagation patterns and can be used to simulate therapeutic interventions like surgery, stimulation, or pharmacological interventions within a unified physiological framework. Notably, model predictions reveal distinct patient-specific responses across interventions, including variable sensitivity to different pharmacological agents and identification of critical regions whose removal or modulation reduced seizure spread.Significance.This framework provides a mechanistic, interpretable approach to simulate and compare individualized treatment strategies. By integrating multimodal data into a unified whole-brain model, it has the potential to improve clinical decision-making in epilepsy by identifying accessible and functionally relevant targets.

This study aimed to investigate different short-term memory capacities (STMC) on resting brain of healthy individuals particularly the neuropsychology and connectivity behaviors. The outcomes may serve as a baseline for clinical diagnosis of memory decline due to aging and mental disorders. It was hypothesized that resting brain of low and typical STMC individuals behaves differently. Thirty-nine healthy young male adults were recruited from local universities. They were categorized as typical or low STMC based on their scores in the Malay Version of the Auditory-Verbal Learning Test (MVAVLT). A resting-state functional magnetic resonance imaging (rs-fMRI) was conducted and data were analyzed using statistical parametric mapping (SPM) and dynamic causal modeling (DCM). Nine neuropsychological assessments were significantly higher (p < 0.05) in typical STMC participants compared with low STMC participants. Four activation clusters survived the contrast “Low > Typical” uncorrected at set and cluster levels threshold (p FWE < 0.05). A causal model containing these clusters as nodes found that there is no preference on negative or positive connectivity among typical and low STMC groups. Nevertheless, implementing a reduced connection scheme revealed more significant connections for the low STMC group. To conclude, the low STMC participants scored lower in all neuropsychological assessments, but a higher activation profile with more areas being connected effectively as compared with the typical STMC group. The results suggest a higher resting neural activity and communication among certain brain areas in low STMC individuals that the brain could have executed as a compensation strategy.

The etiology of transsexualism (TS) remains unknown today because the disease is multifactorial and is caused by a set of factors, including those affecting sexual differentiation of brain tissue during fetal development. Sexual differentiation of the brain has been shown to occur at a much later developmental stage than sexual differentiation of the genitals, and the two processes are regulated independently of each other. Various sexual characteristics, such as gender identity (self-identification of oneself as male or female) and sexual orientation (heterosexuality, homosexuality, or bisexuality), as well as risks of developing neuropsychiatric disorders, are programmed in the brain at an early developmental stage. The structure of certain brain areas in transsexual individuals has been found to differ from that in cisgender men and women and is close, although not identical, to that in humans of the opposite anatomical and genetic sex. Various effects of physiologically active substances on the developing brain have been shown to result in irreversible or partly reversible modification of its neurochemical systems. Family studies have confirmed the role of genetic factors in gender identity disorders. The review provides a detailed analysis of the known loci of candidate genes presumably associated with TS. Both positive and negative correlations with TS have been revealed for most candidate genes, while only negative correlations are known for other markers. The inconsistency of the research results may be due to several factors, including "blurred" samples of transsexuals, the choice of neutral markers lacking the functionally significant polymorphisms that affect their expression and functionality, etc. The review considers the current data on the problem of TS and outlines the possible prospects for further research of the phenomenon at the genetic level with the aim of using the results to verify the diagnosis.

Networks in the parietal and premotor cortices enable essential human abilities regarding motor processing, including attention and tool use. Even though our knowledge on its topography has steadily increased, a detailed picture of hemisphere-specific integrating pathways is still lacking. With the help of multishell diffusion magnetic resonance imaging, probabilistic tractography, and the Graph Theory Analysis, we investigated connectivity patterns between frontal premotor and posterior parietal brain areas in healthy individuals. With a two-stage node characterization approach, we defined the network role of precisely mapped cortical regions from the Julich-Brain atlas. We found evidence for a third, left-sided, medio-dorsal subpathway in a successively graded dorsal stream, referencing more specialized motor processing on the left. Supplementary motor areas had a strongly lateralized connectivity to either left dorsal or right ventral parietal domains, representing an action-attention dichotomy between hemispheres. The left sulcal parietal regions primarily coupled with areas 44 and 45, mirrored by the inferior frontal junction (IFJ) on the right, a structural lateralization we termed as "Broca's-IFJ switch." We were able to deepen knowledge on gyral and sulcal pathways as well as domain-specific contributions in parieto-premotor networks. Our study sheds new light on the complex lateralization of cortical routes for motor activity in the human brain.

Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.

Task-based modulation of functional connectivity of dorsal attention network in adult-ADHD

Music’s role in modulating brain structure, particularly in neurodegenerative contexts such as Alzheimer’s Disease (AD), has been increasingly recognized. While previous studies have hinted at the potential neuroplastic benefits of musical engagement and training, the mechanisms through which music impacts structural connectivity in neurodegenerative pathways remain underexplored. We aimed to examine the impact of music perception skills, active musical engagement, and musical training on structural connectivity in areas relating to memory, emotion, and learning in individuals with worsening memory impairment, investigating the potential neuroplastic effects of music. Employing diffusion tensor imaging (DTI) based structural connectivity and graph theoretical analysis, we investigated brain topological features in 78 participants aged 42 to 85 with a range of memory impairments. Participants were assessed for musical training, engagement, and perception skills. The study analyzed regional and local network topological metrics to examine the influence of musical activities on graph metrics, while controlling for stages of objective memory impairment (SOMI) and diagnosis, separately. This study aimed to elucidate the effects of musical perception skills, active musical engagement, and musical training on structural connectivity within memory, emotion, and learning-related brain areas in individuals with varying degrees of memory impairment. We found enhanced structural connectivity of the right hippocampus and the right posterior cingulate cortex was associated with stronger local network metrics, such as clustering coefficient and betweenness centrality, with increased music perception skills like melody and beat perception. Musical training specifically impacted the clustering coefficient of the right hippocampus and the node degree of the right mid cingulate gyrus. Active musical engagement influenced the eigenvector centrality of the right hippocampus. Furthermore, musical training was associated with enhanced global metrics, such as global efficiency and characteristic path length. Our study integrates diffusion magnetic resonance imaging (MRI) and graph theoretical analysis to reveal significant effects of musical activities on structural connectivity in key brain regions. The results highlight the potential of musical activities to serve as a non-invasive modulatory tool for cognitive resilience, especially in memory impairment and neurodegeneration contexts. These insights contribute to the understanding of delaying AD onset and aiding early-stage patients through music-based interventions, emphasizing the importance of musical engagement in maintaining cognitive and brain health.

Introduction and objective: The purpose of our review was to systematize the knowledge regarding the impact of education on cortical volume and thickness along with assessment of the risk and progress of Alzheimer's disease. We considered the problem among children as well as adults, depending on their level of education and particularized the role of cognitive reserve as a protection from brain damage. Furthermore, we investigate the issue of neuronal tolerance in Alzheimer’s disease. Review methods: The article is a review of 22 original papers, cohort studies and meta-analyses concerning the impact of education on cortical volume and thickness along with the risk and progress of Alzheimer’s disease. Abbreviated description of the state of knowledge: Studies have shown that education is associated with an increase in intelligence, which determines the dynamics of brain cortex changes among children. In adults, the thickness of individual brain areas - primarily the temporal and frontal poles - enhances with the increase of their education level. The dynamics of changes in cortical measurements are correlated with education both in healthy adults and in patients with Alzheimer’s disease. A healthy lifestyle, common for those with higher education, has a positive impact on cortical thickness and lowers the risk of developing Alzeihmer’s disease. Moreover, education comes with better neuronal tolerance for accumulated proteins, yet few studies take notice of more dynamic progress of the disease among adults with higher education. Summary: Research has confirmed the impact of education on cortical volume and thickness. The risk and progress of Alzheimer’s disease are also reflected in the patient’s level of education nevertheless the literature is ambiguous regarding this issue.

The brain is a fundamental organ for the human body to function properly, for which it needs to receive a continuous flow of blood, which explains the existence of control mechanisms that act to maintain this flow as constant as possible in a process known as cerebral autoregulation. One way to obtain information on how the levels of oxygen supplied to the brain vary is through of BOLD (Magnetic Resonance) images, which have the advantage of greater spatial resolution than other forms of measurement, such as transcranial Doppler. However, they do not provide good temporal resolution nor allow for continuous prolonged examination. Thus, it is of great importance to find a method to detect regional differences from short BOLD signals. One of the existing alternatives is complexity measures that can detect changes in the variability and temporal organisation of a signal that could reflect different physiological states. The so-called statistical complexity, created to overcome the shortcomings of entropy alone to explain the concept of complexity, has shown potential with haemodynamic signals. The aim of this study is to determine by using statistical complexity whether it is possible to find differences between physiologically distinct brain areas in healthy individuals. The data set includes BOLD images of 10 people obtained at the University Hospital of Leicester NHS Trust with a 1.5 Tesla magnetic resonance imaging scanner. The data were captured for 180 s at a frequency of 1 Hz. Using various combinations of statistical complexities, no differences were found between hemispheres. However, differences were detected between grey matter and white matter, indicating that these measurements are sensitive to differences in brain tissues.

Background. Given the current realities in Ukraine, particularly the war and the escalating societal tensions, the importance of studying aggression cannot be overstated. However, when examining this phenomenon, it is crucial to avoid oversimplification and seek deeper explanations that account for its multifaceted nature. Generally, there are several explanations for aggression: the genetic explanation, which posits that aggression is influenced by the perinatal effects of genes and their polymorphisms, or their expression through phylogenetic influences; the hormonal explanation, which analyzes aggression through the mechanisms of specific neurotransmission process; and the neuroanatomical explanation, which examines aggression based on the functioning of particular brain areas. The aim of the study was to analyze research conducted over the past 15 years and to provide a comprehensive overview of the factors involved in the development of aggressive behavior by describing various concepts pertaining to its emergence. Methods. In line with the objective of thoroughly covering recent research on diverse mechanisms of aggression, methods of abstraction and information synthesis were employed in the literature analysis. The findings are organized into separate structural elements following a clear framework, starting from perinatal development and work of general neurotransmitter systems to the specific functions of individual brain areas. Results. This study consolidates and structures information from different approaches to the aggression studies. It describes the genetic, hormonal, and neuroanatomical determinants of aggression. The influence of various systems on aggressive behavior is emphasized, whether it is pathological or non-pathological in nature. Conclusions. The challenge of studying aggression as a unified phenomenon stems from the inadequacies of its research mechanisms and the frequent contradictory conclusions regarding the same manifestations. This complicates addressing the root causes of aggression and improving societal psychological well-being. Nevertheless, emphasizing the diversity of its nature and promoting diverse research directions, especially considering varying environments and the imperfections in brain system functions, offers hope for developing targeted methods to address the complex system of aggression. The foundational study of aggression holds promise for furthering a comprehensive theory of aggression that investigates various studies into a cohesive discourse.

All-optical interrogation of millimeter-scale networks and application to developing ferret cortex

The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented opportunities to study the human brain in action as language is read and understood. Recent contextualized language models seem to be able to capture homonymic meaning variation ("bat", in a baseball vs. a vampire context), as well as more nuanced differences of meaning-for example, polysemous words such as "book", which can be interpreted in distinct but related senses ("explain a book", information, vs. "open a book", object) whose differences are fine-grained. We study these subtle differences in lexical meaning along the concrete/abstract dimension, as they are triggered by verb-noun semantic composition. We analyze functional magnetic resonance imaging (fMRI) activations elicited by Italian verb phrases containing nouns whose interpretation is affected by the verb to different degrees. By using a contextualized language model and human concreteness ratings, we shed light on where in the brain such fine-grained meaning variation takes place and how it is coded. Our results show that phrase concreteness judgments and the contextualized model can predict BOLD activation associated with semantic composition within the language network. Importantly, representations derived from a complex, nonlinear composition process consistently outperform simpler composition approaches. This is compatible with a holistic view of semantic composition in the brain, where semantic representations are modified by the process of composition itself. When looking at individual brain areas, we find that encoding performance is statistically significant, although with differing patterns of results, suggesting differential involvement, in the posterior superior temporal sulcus, inferior frontal gyrus and anterior temporal lobe, and in motor areas previously associated with processing of concreteness/abstractness.

In this article, Pessoa emphasizes the importance of viewing neural activity from a perspective that functional networks form dynamically in a way that dramatically changes the functional contribution of individual brain areas. In this response, I argue that we should strive toward pluralism in understanding neural activity at both the emergent network and modular levels, on the bases that a purely emergent understanding would be incomplete, and that there are computational advantages to anatomically stable modularity.

The current and future contribution of neuroimaging to the understanding of disorders of consciousness

Memory encoding and retrieval rely on specific interactions across multiple brain areas. Although connections between individual brain areas have been extensively studied, the anatomical and functional specificity of neuronal circuit organization underlying information transfer across multiple brain areas remains unclear. Here, we combine transsynaptic viral tracing, optogenetic manipulations, and calcium dynamics recordings to dissect the multisynaptic functional connectivity of the amygdala. We identify a distinct basolateral amygdala (BLA) subpopulation that connects disynaptically to the periaqueductal gray (PAG) via the central amygdala (CeA). This disynaptic pathway serves as a core circuit element necessary for the learning and expression of conditioned fear and exhibits learning-related plasticity. Together, our findings demonstrate the utility of multisynaptic approaches for functional circuit analysis and indicate that disynaptic specificity may be a general feature of neuronal circuit organization.

Altered brain functional connectivity has been observed in conditions such as schizophrenia, dementia and depression and may represent a target for treatment. Transcutaneous vagus nerve stimulation (tVNS) is a form of non-invasive brain stimulation that is increasingly used in the treatment of a variety of health conditions. We previously combined tVNS with magnetoencephalography (MEG) and observed that various stimulation frequencies affected different brain areas in healthy individuals. We further investigated whether tVNS had an effect on functional connectivity with a focus on brain regions associated with mood. We compared functional connectivity (whole-head and region of interest) in response to four stimulation frequencies of tVNS using data collected from concurrent MEG and tVNS in 17 healthy participants using Weighted Phase Lag Index (WPLI) to calculate correlation between brain areas. Different frequencies of stimulation lead to changes in functional connectivity across multiple regions, notably areas linked to the default mode network (DMN), salience network (SN) and the central executive network (CEN). It was observed that tVNS delivered at a frequency of 24 Hz was the most effective in increasing functional connectivity between these areas and sub-networks in healthy participants. Our results indicate that tVNS can alter functional connectivity in regions that have been associated with mood and memory disorders. Varying the stimulation frequency led to alterations in different brain areas, which may suggest that personalized stimulation protocols can be developed for the targeted treatment of different medical conditions using tVNS.

It remains unclear how brain metabolic activities transform in response to dopamine deficiency in the prodromal and early phases of Parkinson's disease (PD). To investigate the relationship between nigrostriatal dopaminergic denervation and brain glucose metabolism in patients with isolated rapid eye movement sleep behavior disorder (iRBD) and early PD. This cohort study included 28 patients with polysomnography-confirmed iRBD, 24 patients with de novo PD with probable rapid eye movement sleep behavior disorder (denovo PD), and 28 healthy controls (HCs) who underwent two positron emission tomography scans with 18 F-fluorodeoxyglucose (all participants) and 18 F-N-3-fluoropropyl-2β-carboxymethoxy-3β-(4-iodophenyl)-nortropane (except for one denovo PD patient and 15 HCs). We analyzed striatal and voxel-wise whole-brain glucose metabolism in relation to nigrostriatal dopaminergic integrity and comparatively investigated the whole-brain metabolic connectivity among the groups. We also assessed longitudinal metabolic changes against progressive dopaminergic denervation over 4 years in the iRBD group. From HCs to iRBD and finally to the denovo PD, dopaminergic integrity positively correlated with metabolic activity in the caudate, whereas a negative correlation was observed in the posterior putamen. In the iRBD group, there was a metabolic increase in the inferior orbitofrontal cortex against putaminal dopaminergic denervation at baseline, but negative correlations were newly observed in the superior orbitofrontal cortex and superior frontal gyrus at the 4-year follow-up. The denovo PD group showed negative correlations in the cerebellum and fusiform gyrus. Intra- and inter-regional metabolic connectivities in the parieto-occipital cortices were enhanced in the iRBD group compared with the denovo PD and HC groups. In the iRBD group, overall metabolic connectivity was strengthened along with enhanced basal ganglia-frontal connection by advancing dopaminergic denervation. Our findings suggest diverse trajectories of metabolic responses associated with dopaminergic denervation between individual brain areas in the prodromal and early PD stages. © 2022 International Parkinson and Movement Disorder Society.

BackgroundThe representation of variability in sensitivity to pain by differences in neural connectivity patterns and its association with psychological factors needs further investigation. This study assessed differences in resting-state functional connectivity (rsFC) and its association to cognitive-affective aspects of pain in two groups of healthy subjects with low versus high sensitivity to pain (LSP vs. HSP). We hypothesized that HSP will show stronger connectivity in brain regions involved in the affective-motivational processing of pain and that this higher connectivity would be related to negative affective and cognitive evaluations of pain.MethodsForty-eight healthy subjects were allocated to two groups according to their tolerability to cold stimulation (cold pressor test, CPT, 1°C). Group LSP (N = 24) reached the cut-off time of 180±0 sec and group HSP tolerated the CPT for an average of 13±4.8 sec. Heat, cold and mechanical evoked pain were measured, as well as pain-catastrophizing (PCS), depression, anxiety and stress (DASS-21). All subjects underwent resting state fMRI. ROI-to-ROI analysis was performed.ResultsIn comparison to the LSP, the HSP had stronger interhemispheric connectivity of the amygdala (p = 0.01) and between the amygdala and nucleus accumbens (NAc) (p = 0.01). Amygdala connectivity was associated with higher pain catastrophizing in the HSP only (p<0.01).ConclusionsThese findings suggest that high sensitivity to pain may be reflected by neural circuits involved in affective and motivational aspects of pain. To what extent this connectivity within limbic brain structures relates to higher alertness and more profound withdrawal behavior to aversive events needs to be further investigated.

Though the organization of functional brain networks is modular at its core, modularity does not capture the full range of dynamic interactions between individual brain areas nor at the level of subnetworks. In this paper we present a hierarchical model that represents both flexible and modular aspects of intrinsic brain organization across time by constructing spatiotemporally flexible subnetworks. We also demonstrate that segregation and integration are complementary and simultaneous events. The method is based on combining the instantaneous phase synchrony analysis (IPSA) framework with community detection to identify a small, yet representative set of subnetwork components at the finest level of spatial granularity. At the next level, subnetwork components are combined into spatiotemporally flexibly subnetworks where temporal lag in the recruitment of areas within subnetworks is captured. Since individual brain areas are permitted to be part of multiple interleaved subnetworks, both modularity as well as more flexible tendencies of connectivity are accommodated for in the model. Importantly, we show that assignment of subnetworks to the same community (integration) corresponds to positive phase coherence within and between subnetworks, while assignment to different communities (segregation) corresponds to negative phase coherence or orthogonality. Together with disintegration, i.e. the breakdown of internal coupling within subnetwork components, orthogonality facilitates reorganization between subnetworks. In addition, we show that the duration of periods of integration is a function of the coupling strength within subnetworks and subnetwork components which indicates an underlying metastable dynamical regime. Based on the main tendencies for either integration or segregation, subnetworks are further clustered into larger meta-networks that are shown to correspond to combinations of core resting-state networks. We also demonstrate that subnetworks and meta-networks are coarse graining strategies that captures the quasi-cyclic recurrence of global patterns of integration and segregation in the brain. Finally, the method allows us to estimate in broad terms the spectrum of flexible and/or modular tendencies for individual brain areas.

Developmental dyslexia (DD) is the most prevalent neurodevelopmental disorder with a substantial negative influence on the individual’s academic achievement and career. Research on its neuroanatomical origins has continued for half a century, yielding, however, inconsistent results, lowered total brain volume being the most consistent finding. We set out to evaluate the grey matter (GM) volume and cortical abnormalities in adult dyslexic individuals, employing a combination of whole-brain voxel- and surface-based morphometry following current recommendations on analysis approaches, coupled with rigorous neuropsychological testing. Whilst controlling for age, sex, total intracranial volume, and performance IQ, we found both decreased GM volume and cortical thickness in the left insula in participants with DD. Moreover, they had decreased GM volume in left superior temporal gyrus, putamen, globus pallidus, and parahippocampal gyrus. Higher GM volumes and cortical thickness in these areas correlated with better reading and phonological skills, deficits of which are pivotal to DD. Crucially, total brain volume did not influence our results, since it did not differ between the groups. Our findings demonstrating abnormalities in brain areas in individuals with DD, which previously were associated with phonological processing, are compatible with the leading hypotheses on the neurocognitive origins of DD.