Human connectomics

Functional connectivity of the pediatric brain

PURPOSE

This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders.

METHODS

The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents.

RESULTS

Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders.

CONCLUSION

Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.

Similarity in evoked responses does not imply similarity in macroscopic network states

It is commonplace in neuroscience to assume that if two tasks activate the same brain areas in the same way, then they are recruiting the same underlying networks. Yet computational theory has shown that the same pattern of activity can emerge from many different underlying network representations. Here we evaluated whether similarity in activation necessarily implies similarity in network architecture by comparing region-wise activation patterns and functional correlation profiles from a large sample of healthy subjects (N = 242). Participants performed two executive control tasks known to recruit nearly identical brain areas, the color-word Stroop task and the Multi-Source Interference Task (MSIT). Using a measure of instantaneous functional correlations, based on edge time series, we estimated the task-related networks that differed between incongruent and congruent conditions. We found that the two tasks were much more different in their network profiles than in their evoked activity patterns at different analytical levels, as well as for a wide range of methodological pipelines. Our results reject the notion that having the same activation patterns means two tasks engage the same underlying representations, suggesting that task representations should be independently evaluated at both node and edge (connectivity) levels.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

DCP: A pipeline toolbox for diffusion connectome

The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.

Sex-dependent differences in connectivity patterns are related to episodic memory recall

Previous studies have shown that females typically outperform males on episodic memory tasks. In this study, we investigated if (1) there are differences between males and females in their connectome characteristics, (2) if these connectivity patterns are associated with memory performance, and (3) if these brain connectome characteristics contribute to the differences in episodic memory performance between sexes. In a sample of 655 healthy young subjects (n = 391 females; n = 264 males), we derived brain network characteristics from diffusion-weighted imaging (DWI) data using models of crossing fibers within each voxel of the brain and probabilistic tractography (graph strength, shortest path length, global efficiency, and weighted transitivity). Group differences were analysed with linear models and mediation analyses were used to explore how connectivity patterns might relate to sex-dependent differences in memory performance. Our results show significant sex-dependent differences in weighted transitivity (d = 0.42), with males showing higher values. Further, we observed a negative association between weighted transitivity and memory performance (r = -0.12). Finally, these distinct connectome characteristics partially mediated the observed differences in memory performance (effect size of the indirect effect r = 0.02). Our findings indicate a higher interconnectedness in females compared to males. Additionally, we demonstrate that the sex-dependent differences in episodic memory performance can be partially explained by the differences in this connectome measure. These results further underscore the importance of sex-dependent differences in brain connectivity and their impact on cognitive function.

Parcellation-Based Connectivity Model of the Judgement Core

Judgement is a higher-order brain function utilized in the evaluation process of problem solving. However, heterogeneity in the task methodology based on the many definitions of judgement and its expansive and nuanced applications have prevented the identification of a unified cortical model at a level of granularity necessary for clinical translation. Forty-six task-based fMRI studies were used to generate activation-likelihood estimations (ALE) across moral, social, risky, and interpersonal judgement paradigms. Cortical parcellations overlapping these ALEs were used to delineate patterns in neurocognitive network engagement for the four judgement tasks. Moral judgement involved the bilateral superior frontal gyri, right temporal gyri, and left parietal lobe. Social judgement demonstrated a left-dominant frontoparietal network with engagement of right-sided temporal limbic regions. Moral and social judgement tasks evoked mutual engagement of the bilateral DMN. Both interpersonal and risk judgement were shown to involve a right-sided frontoparietal network with accompanying engagement of the left insular cortex, converging at the right-sided CEN. Cortical activation in normophysiological judgement function followed two separable patterns involving the large-scale neurocognitive networks. Specifically, the DMN was found to subserve judgement centered around social inferences and moral cognition, while the CEN subserved tasks involving probabilistic reasoning, risk estimation, and strategic contemplation.

Tissues as networks of cells: towards generative rules of complex organ development

Network analysis is a well-known and powerful tool in molecular biology. More recently, it has been introduced in developmental biology. Tissues can be readily translated into spatial networks such that cells are represented by nodes and intercellular connections by edges. This discretization of cellular organization enables mathematical approaches rooted in network science to be applied towards the understanding of tissue structure and function. Here, we describe how such tissue abstractions can enable the principles that underpin tissue formation and function to be uncovered. We provide an introduction into biologically relevant network measures, then present an overview of different areas of developmental biology where these approaches have been applied. We then summarize the general developmental rules underpinning tissue topology generation. Finally, we discuss how generative models can help to link the developmental rule back to the tissue topologies. Our collection of results points at general mechanisms as to how local developmental rules can give rise to observed topological properties in multicellular systems.

EEG based brain functional connectivity analysis for post-autoimmune encephalitis (AE) patients with epilepsy

Autoimmune Encephalitis (AE) refers to a group of conditions that occur when the body's immune system mistakenly attacks healthy brain cells, leading to inflammation of the brain. Seizures are a common symptom of AE and more than a third of patients experiencing seizures secondary to AE become epileptic over time. The objective of the present study is to identify biomarkers that can be used to identify those patients in whom AE will evolve into epilepsy. The bursts of abnormal electrical activity that occur during a seizure can be recorded by using Electroencephalography (EEG). In this work, common EEG (cEEG) and ambulatory EEG (aEEG) were recorded to compare the brain functional connectivity (FC) properties in post-AE patients with epilepsy patients and post-AE patients without epilepsy. The brain functional networks of spike waves were first constructed on the basis of Phase Locking Value (PLV). An analysis was then performed of the differences which existed in the FC properties of clustering coefficient, characteristic path length, global efficiency, local efficiency, and node degree between post-AE patients with epilepsy patients and post-AE patients without epilepsy. From the perspective of brain functional network analysis, post-AE patients with epilepsy showed a more complex network structure. Furthermore, the five FC properties have been found signification different, all FC property values of post-AE patients with epilepsy are higher than those of post-AE patients without epilepsy of cEEG and aEEG. Based on the extracted FC properties, five classifiers were used to classify them, and the results showed that all five FC properties could effectively distinguish between post-AE patients with epilepsy patients and post-AE patients without epilepsy in both cEEG and aEEG. These findings are potentially helpful for diagnosing whether a patient with AE will become epileptic.

Flexibility of in vitro cortical circuits influences resilience from microtrauma

Background

Small clusters comprising hundreds to thousands of neurons are an important level of brain architecture that correlates single neuronal properties to fulfill brain function, but the specific mechanisms through which this scaling occurs are not well understood. In this study, we developed an in vitro experimental platform of small neuronal circuits (islands) to probe the importance of structural properties for their development, physiology, and response to microtrauma.

Methods

Primary cortical neurons were plated on a substrate patterned to promote attachment in clusters of hundreds of cells (islands), transduced with GCaMP6f, allowed to mature until 10-13 days in vitro (DIV), and monitored with Ca²⁺ as a non-invasive proxy for electrical activity. We adjusted two structural factors-island size and cellular density-to evaluate their role in guiding spontaneous activity and network formation in neuronal islands.

Results

We found cellular density, but not island size, regulates of circuit activity and network function in this system. Low cellular density islands can achieve many states of activity, while high cellular density biases islands towards a limited regime characterized by low rates of activity and high synchronization, a property we summarized as "flexibility." The injury severity required for an island to lose activity in 50% of its population was significantly higher in low-density, high flexibility islands.

Conclusion

Together, these studies demonstrate flexible living cortical circuits are more resilient to microtrauma, providing the first evidence that initial circuit state may be a key factor to consider when evaluating the consequences of trauma to the cortex.

A new model for freedom of movement using connectomic analysis

The problem of whether we can execute free acts or not is central in philosophical thought, and it has been studied by numerous scholars throughout the centuries. Recently, neurosciences have entered this topic contributing new data and insights into the neuroanatomical basis of cognitive processes. With the advent of connectomics, a more refined landscape of brain connectivity can be analysed at an unprecedented level of detail. Here, we identify the connectivity network involved in the movement process from a connectomics point of view, from its motivation through its execution until the sense of agency develops. We constructed a "volitional network" using data derived from the Brainnetome Atlas database considering areas involved in volitional processes as known in the literature. We divided this process into eight processes and used Graph Theory to measure several structural properties of the network. Our results show that the volitional network is small-world and that it contains four communities. Nodes of the right hemisphere are contained in three of these communities whereas nodes of the left hemisphere only in two. Centrality measures indicate the nucleus accumbens is one of the most connected nodes in the network. Extensive connectivity is observed in all processes except in Decision (to move) and modulation of Agency, which might correlate with a mismatch mechanism for perception of Agency.

The connectome from the cerebral cortex to skeletal muscle using viral transneuronal tracers: a review

Connectomics has developed from an initial observation under an electron microscope to the present well-known medical imaging research approach. The emergence of the most popular transneuronal tracers has further advanced connectomics research. Researchers use the virus trans-nerve tracing method to trace the whole brain, mark the brain nerve circuit and nerve connection structure, and construct a complete nerve conduction pathway. This review assesses current methods of studying cortical to muscle connections using viral neuronal tracers and demonstrates their application in disease diagnosis and prognosis.

Advances in Neuroimaging and Monitoring to Defend Cerebral Perfusion in Noncardiac Surgery

Noncardiac surgery conveys a substantial risk of secondary organ dysfunction and injury. Neurocognitive dysfunction and covert stroke are emerging as major forms of perioperative organ dysfunction, but a better understanding of perioperative neurobiology is required to identify effective treatment strategies. The likelihood and severity of perioperative brain injury may be increased by intraoperative hemodynamic dysfunction, tissue hypoperfusion, and a failure to recognize complications early in their development. Advances in neuroimaging and monitoring techniques, including optical, sonographic, and magnetic resonance, have progressed beyond structural imaging and now enable noninvasive assessment of cerebral perfusion, vascular reserve, metabolism, and neurologic function at the bedside. Translation of these imaging methods into the perioperative setting has highlighted several potential avenues to optimize tissue perfusion and deliver neuroprotection. This review introduces the methods, metrics, and evidence underlying emerging optical and magnetic resonance neuroimaging methods and discusses their potential experimental and clinical utility in the setting of noncardiac surgery.

Overview of (f)MRI Studies of Cognitive Aging for Non-Experts: Looking through the Lens of Neuroimaging

This special issue concerning Brain Functional and Structural Connectivity and Cognition aims to expand our understanding of brain connectivity. Herein, I review related topics including the principle and concepts of functional MRI, brain activation, and functional/structural connectivity in aging for uninitiated readers. Visuospatial attention, one of the well-studied functions in aging, is discussed from the perspective of neuroimaging.

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/.

A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys

The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.

Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks

The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 - 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.

Backscattering polarimetric imaging of the human brain to determine the orientation and degree of alignment of nerve fiber bundles

The nerve fiber bundles constitutive of the white matter in the brain are organized in such a way that they exhibit a certain degree of structural anisotropy and birefringence. The birefringence exhibited by such aligned fibrous tissue is known to be extremely sensitive to small pathological alterations. Indeed, highly aligned anisotropic fibers exhibit higher birefringence than structures with weaker alignment and anisotropy, such as cancerous tissue. In this study, we performed experiments on thick coronal slices of a healthy human brain to explore the possibility of (i) measuring, with a polarimetric microscope the birefringence exhibited by the white matter and (ii) relating the measured birefringence to the fiber orientation and the degree of alignment. This is done by analyzing the spatial distribution of the degree of polarization of the backscattered light and its variation with the polarization state of the probing beam. We demonstrate that polarimetry can be used to reliably distinguish between white and gray matter, which might help to intraoperatively delineate unstructured tumorous tissue and well organized healthy brain tissue. In addition, we show that our technique is able to sensitively reconstruct the local mean nerve fiber orientation in the brain, which can help to guide tumor resections by identifying vital nerve fiber trajectories thereby improving the outcome of the brain surgery.

The organization of functional neurocognitive networks in focal epilepsy correlates with domain-specific cognitive performance

Understanding and diagnosing cognitive impairment in epilepsy remains a prominent challenge. New etiological models suggest that cognitive difficulties might not be directly linked to seizure activity, but are rather a manifestation of a broader brain pathology. Consequently, treating seizures is not sufficient to alleviate cognitive symptoms, highlighting the need for novel diagnostic tools. Here, we investigated whether the organization of three intrinsic, resting-state functional connectivity networks was correlated with domain-specific cognitive test performance. Using individualized EEG source reconstruction and graph theory, we examined the association between network small worldness and cognitive test performance in 23 patients with focal epilepsy and 17 healthy controls, who underwent a series of standardized pencil-and-paper and digital cognitive tests. We observed that the specific networks robustly correlated with test performance in distinct cognitive domains. Specifically, correlations were evident between the default mode network and memory in patients, the central-executive network and executive functioning in controls, and the salience network and social cognition in both groups. Interestingly, the correlations were evident in both groups, but in different domains, suggesting an alteration in these functional neurocognitive networks in focal epilepsy. The present findings highlight the potential clinical relevance of functional brain network dysfunction in cognitive impairment.

Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsy

OBJECTIVE

The hypersynchronous neuronal activity associated with epilepsy causes widespread functional network disruptions extending beyond the epileptogenic zone. This altered network topology is considered a mediator for non-seizure symptoms, such as cognitive impairment. The aim of this study was to investigate functional network alterations in focal epilepsy patients with good seizure control and high quality of life.

METHODS

We compared twenty-two focal epilepsy patients and sixteen healthy controls on graph metrics derived from functional connectivity of source-level resting-state EEG. Graph metrics were calculated over a range of network densities in five frequency bands.

RESULTS

We observed a significantly increased small world index in patients relative to controls. On the local level, two left-hemisphere regions displayed a shift towards greater alpha band "hubness". The findings were not mediated by age, sex or education, nor by age of epilepsy onset, duration or focus lateralisation.

CONCLUSIONS

Widespread functional network alterations are evident in focal epilepsy, even in a cohort characterised by successful anti-seizure medication therapy and high quality of life. These findings might support the position that functional network analysis could hold clinical relevance for epilepsy.

SIGNIFICANCE

Focal epilepsy is accompanied by global and local functional network aberrancies which might be implied in the sustenance of non-seizure symptoms.

Distinct Functional and Structural Connectivity of the Human Hand-Knob Supported by Intraoperative Findings

Fine motor skills rely on the control of hand muscles exerted by a region of primary motor cortex (M1) that has been extensively investigated in monkeys. Although neuroimaging enables the exploration of this system also in humans, indirect measurements of brain activity prevent causal definitions of hand motor representations, which can be achieved using data obtained during brain mapping in tumor patients. High-frequency direct electrical stimulation delivered at rest (HF-DES-Rest) on the hand-knob region of the precentral gyrus has identified two sectors showing differences in cortical excitability. Using quantitative analysis of motor output elicited with HF DES-Rest, we characterized two sectors based on their excitability, higher in the posterior and lower in the anterior sector. We studied whether the different cortical excitability of these two regions reflected differences in functional connectivity (FC) and structural connectivity (SC). Using healthy adults from the Human Connectome Project (HCP), we computed FC and SC of the anterior and the posterior hand-knob sectors identified within a large cohort of patients. The comparison of FC of the two seeds showed that the anterior hand-knob, relative to the posterior hand-knob, showed stronger functional connections with a bilateral set of parietofrontal areas responsible for integrating perceptual and cognitive hand-related sensorimotor processes necessary for goal-related actions. This was reflected in different patterns of SC between the two sectors. Our results suggest that the human hand-knob is a functionally and structurally heterogeneous region organized along a motor-cognitive gradient.SIGNIFICANCE STATEMENT The capability to perform complex manipulative tasks is one of the major characteristics of primates and relies on the fine control of hand muscles exerted by a highly specialized region of the precentral gyrus, often termed the "hand-knob" sector. Using intraoperative brain mapping, we identify two hand-knob sectors (posterior and anterior) characterized by differences in cortical excitability. Based on resting-state functional connectivity (FC) and tractography in healthy subjects, we show that posterior and anterior hand-knob sectors differ in their functional connectivity (FC) and structural connectivity (SC) with frontoparietal regions. Thus, anteroposterior differences in cortical excitability are paralleled by differences in FC and SC that likely reflect a motor (posterior) to cognitive (anterior) organization of this cortical region.

Diffusion properties of the fornix assessed by deterministic tractography shows age, sex, volume, cognitive, hemispheric, and twin relationships in young adults from the Human Connectome Project

The fornix is the primary efferent pathway of the hippocampus and plays a central role in memory circuitry. Diffusion tensor imaging has shown changes in the fornix with typical development and aging. Here, the fornix was investigated in 903 healthy young adult participants aged 22-36 years old from the high-spatial resolution 1.25 mm isotropic Human Connectome Project (HCP) diffusion dataset. Manual deterministic tractography was used to assess relationships between fornix diffusion parameters and age, sex, laterality, hippocampus volume, memory scores, and genetic effects in a subgroup of mono- and dizygotic twins. Fornix diffusion metrics were weakly correlated with age over the given age span. While significant hemispheric and sex differences were observed (greater fractional anisotropy (FA) and volume in the right hemisphere; greater FA and volume in females), there was great overlap between the groups. Hippocampus volume measurements showed greater volume in the right hemisphere, were found to be larger in males, and were weakly correlated with fornix FA and volume. Interestingly, all fornix diffusion measurements correlated strongly with fornix volume, suggesting the presence of partial volume effects despite the high-spatial resolution of the data. Both fornix diffusion parameters and hippocampal volumes were able to explain some variance (0.6-5.5%) in the memory tests evaluated. The fornix diffusion parameters were influenced by both genetic and shared environmental factors, displaying greater variability in dizygotic than in monozygotic twins. These findings provide a comprehensive depiction of the fornix in healthy, young individuals, upon which future typical development/aging and pathological studies could anchor.

Resting-State Static and Dynamic Functional Abnormalities in Active Professional Fighters With Repetitive Head Trauma and With Neuropsychological Impairments

Previous neuroimaging studies have identified structural brain abnormalities in active professional fighters with repetitive head trauma and correlated these changes with fighters' neuropsychological impairments. However, functional brain changes in these fighters derived using neuroimaging techniques remain unclear. In this study, both static and dynamic functional connectivity alterations were investigated (1) between healthy normal control subjects (NC) and fighters and (2) between non-impaired and impaired fighters. Resting-state fMRI data were collected on 35 NC and 133 active professional fighters, including 68 impaired fighters and 65 non-impaired fighters, from the Professional Fighters Brain Health Study at our center. Impaired fighters performed worse on processing speed (PSS) tasks with visual-attention and working-memory demands. The static functional connectivity (sFC) matrix was estimated for every pair of regions of interest (ROI) using a subject-specific parcellation. The dynamic functional connectivity (dFC) was estimated using a sliding-window method, where the variability of each ROI pair across all windows represented the temporal dynamics. A linear regression model was fitted for all 168 subjects, and different t-contrast vectors were used for between-group comparisons. An association analysis was further conducted to evaluate FC changes associated with PSS task performances without creating artificial impairment group-divisions in fighters. Following corrections for multiple comparisons using network-based statistics, our study identified significantly reduced long-range frontal-temporal, frontal-occipital, temporal-occipital, and parietal-occipital sFC strengths in fighters than in NCs, corroborating with previously observed structural damages in corresponding white matter tracts in subjects experiencing repetitive head trauma. In impaired fighters, significantly decreased sFC strengths were found among key regions involved in visual-attention, executive and cognitive process, as compared to non-impaired fighters. Association analysis further reveals similar sFC deficits to worse PSS task performances in all 133 fighters. With our choice of dFC indices, we were not able to observe any significant dFC changes beyond a trend-level increased temporal variability among similar regions with weaker sFC strengths in impaired fighters. Collectively, our functional brain findings supplement previously reported structural brain abnormalities in fighters and are important to comprehensively understand brain changes in fighters with repetitive head trauma.

Organized Resting-state Functional Dysconnectivity of the Prefrontal Cortex in Patients with Schizophrenia

Schizophrenia has prominent functional dysconnectivity, especially in the prefrontal cortex (PFC). However, it is unclear whether in the same group of patients with schizophrenia, PFC functional dysconnectivity appears in an organized manner or is stochastically located in different subregions. By investigating the resting-state functional connectivity (rsFC) of each PFC subregion from the Brainnetome atlas in 40 schizophrenia patients and 40 healthy subjects, we found 24 altered connections in schizophrenia, and the connections were divided into four categories by a clustering analysis: increased connections within the PFC, increased connections between the inferior PFC and the thalamus/striatum, reduced connections between the PFC and the motor control areas, and reduced connections between the orbital PFC and the emotional perception regions. In addition, the four categories of rsFC showed distinct cognitive engagement patterns. Our findings suggest that PFC subregions have specific functional dysconnectivity patterns in schizophrenia and may reflect heterogeneous symptoms and cognitive deficits in schizophrenia.

Early-Stage Identification and Pathological Development of Alzheimer's Disease Using Multimodal MRI

Alzheimer's disease (AD) is one of the most common progressive and irreversible neurodegenerative diseases. The study of the pathological mechanism of AD and early-stage diagnosis is essential and important. Subjective cognitive decline (SCD), the first at-risk stage of AD occurring prior to amnestic mild cognitive impairment (aMCI), is of great research value and has gained our interest. To investigate the entire pathological development of AD pathology efficiently, we proposed a machine learning classification method based on a multimodal support vector machine (SVM) to investigate the structural and functional connectivity patterns of the three stages of AD (SCD, aMCI, and AD). Our experiments achieved an accuracy of 98.58% in the AD group, 97.76% in the aMCI group, and 80.24% in the SCD group. Moreover, in our experiments, we identified the most discriminating brain regions, which were mainly located in the default mode network and subcortical structures (SCS). Notably, with the development of AD pathology, SCS regions have become increasingly important, and structural connectivity has shown more discriminative power than functional connectivity. The current study may shed new light on the pathological mechanism of AD and suggests that whole-brain connectivity may provide potential effective biomarkers for the early-stage diagnosis of AD.

Towards deep learning for connectome mapping: A block decomposition framework

We propose a new framework to map structural connectomes using deep learning and diffusion MRI. We show that our framework not only enables connectome mapping with a convolutional neural network (CNN), but can also be straightforwardly incorporated into conventional connectome mapping pipelines to enhance accuracy. Our framework involves decomposing the entire brain volume into overlapping blocks. Blocks are sufficiently small to ensure that a CNN can be efficiently trained to predict each block's internal connectivity architecture. We develop a block stitching algorithm to rebuild the full brain volume from these blocks and thereby map end-to-end connectivity matrices. To evaluate our block decomposition and stitching (BDS) framework independent of CNN performance, we first map each block's internal connectivity using conventional streamline tractography. Performance is evaluated using simulated diffusion MRI data generated from numerical connectome phantoms with known ground truth connectivity. Due to the redundancy achieved by allowing blocks to overlap, we find that our block decomposition and stitching steps per se can enhance the accuracy of probabilistic and deterministic tractography algorithms by up to 20-30%. Moreover, we demonstrate that our framework can improve the strength of structure-function coupling between in vivo diffusion and functional MRI data. We find that structural brain networks mapped with deep learning correlate more strongly with functional brain networks (r ​= ​0.45) than those mapped with conventional tractography (r ​= ​0.36). In conclusion, our BDS framework not only enables connectome mapping with deep learning, but its two constituent steps can be straightforwardly incorporated as part of conventional connectome mapping pipelines to enhance accuracy.

Diffusion Weighted and Diffusion Tensor MRI in Pediatric Neuroimaging Including Connectomics: Principles and Applications

Diffusion weighted MRI (DWI) including diffusion tensor imaging (DTI) are unique imaging techniques that render qualitative and quantitative information of the central nervous system white matter (WM) ultrastructure. It uses the Brownian movement of water molecules to probe tissue microstructure. It is a noninvasive method, with superb sensitivity to the differential mobility of water molecules within various components of the brain without the necessity to inject contrast agents. By sampling the 3 dimensional shape, direction and magnitude of the water diffusion, DWI/DTI generates unique tissue contrasts that can be used to study the axonal WM organization of the central nervous system. Its application allows to study the normal and anomalous brain development including connectivity, as well as a multitude of WM diseases. This article discusses/summarizes the principles of DWI/DTI and its applications in pediatric neuroscience research.

Categorical representation from sound and sight in the ventral occipito-temporal cortex of sighted and blind

Is vision necessary for the development of the categorical organization of the Ventral Occipito-Temporal Cortex (VOTC)? We used fMRI to characterize VOTC responses to eight categories presented acoustically in sighted and early blind individuals, and visually in a separate sighted group. We observed that VOTC reliably encodes sound categories in sighted and blind people using a representational structure and connectivity partially similar to the one found in vision. Sound categories were, however, more reliably encoded in the blind than the sighted group, using a representational format closer to the one found in vision. Crucially, VOTC in blind represents the categorical membership of sounds rather than their acoustic features. Our results suggest that sounds trigger categorical responses in the VOTC of congenitally blind and sighted people that partially match the topography and functional profile of the visual response, despite qualitative nuances in the categorical organization of VOTC between modalities and groups.

The world is full of rich and dynamic visual information. To avoid information overload, the human brain groups inputs into categories such as faces, houses, or tools. A part of the brain called the ventral occipito-temporal cortex (VOTC) helps categorize visual information. Specific parts of the VOTC prefer different types of visual input; for example, one part may tend to respond more to faces, whilst another may prefer houses. However, it is not clear how the VOTC characterizes information.

One idea is that similarities between certain types of visual information may drive how information is organized in the VOTC. For example, looking at faces requires using central vision, while looking at houses requires using peripheral vision. Furthermore, all faces have a roundish shape while houses tend to have a more rectangular shape. Another possibility, however, is that the categorization of different inputs cannot be explained just by vision, and is also be driven by higher-level aspects of each category. For instance, how humans use or interact with something may also influence how an input is categorized. If categories are established depending (at least partially) on these higher-level aspects, rather than purely through visual likeness, it is likely that the VOTC would respond similarly to both sounds and images representing these categories.

Now, Mattioni et al. have tested how individuals with and without sight respond to eight different categories of information to find out whether or not categorization is driven purely by visual likeness. Each category was presented to participants using sounds while measuring their brain activity. In addition, a group of participants who could see were also presented with the categories visually. Mattioni et al. then compared what happened in the VOTC of the three groups – sighted people presented with sounds, blind people presented with sounds, and sighted people presented with images – in response to each category.

The experiment revealed that the VOTC organizes both auditory and visual information in a similar way. However, there were more similarities between the way blind people categorized auditory information and how sighted people categorized visual information than between how sighted people categorized each type of input. Mattioni et al. also found that the region of the VOTC that responds to inanimate objects massively overlapped across the three groups, whereas the part of the VOTC that responds to living things was more variable.

These findings suggest that the way that the VOTC organizes information is, at least partly, independent from vision. The experiments also provide some information about how the brain reorganizes in people who are born blind. Further studies may reveal how differences in the VOTC of people with and without sight affect regions typically associated with auditory categorization, and potentially explain how the brain reorganizes in people who become blind later in life.

A regularized and smoothed General Linear Kalman Filter for more accurate estimation of time-varying directed connectivity

Adaptive algorithms based on the Kalman filter are valuable tools to model the dynamic and directed Granger causal interactions between neurophysiological signals simultaneously recorded from multiple cortical regions. Among these algorithms, the General Linear Kalman Filter (GLKF) has proven to be the most accurate and reliable. Here we propose a regularized and smoothed GLKF (spsm-GLKF) with ℓ1 norm penalties based on lasso or group lasso and a fixedinterval smoother. We show that the group lasso penalty promotes sparse solutions by shrinking spurious connections to zero, while the smoothing increases the robustness of the estimates. Overall, our results demonstrate that spsm-GLKF outperforms the original GLKF, and represents a more accurate tool for the characterization of dynamical and sparse functional brain networks.

Nanostructured Modulators of Neuroglia

Biological and synthetic nanostructures can influence both glia and neurons in the central nervous system. Neurons represent only a small proportion (about 10%) of cells in the brain, whereas glial cells are the most abundant cell type. Non-targeted nanomedicines are mainly internalized by glia, in particular microglia, and to a lesser extent by astrocytes. Internalized nanomedicines by glia indirectly modify the functional status of neurons. The mechanisms of biochemical, morphological and functional changes of neural cells exposed to nanomedicines are still not well-understood. This minireview provides a cross-section of morphological and biochemical changes in glial cells and neurons exposed to different classes of hard and soft nanostructures.

Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function

Functional magnetic resonance imaging has proved to be a powerful tool to characterize spatiotemporal patterns of human brain activity. Analysis methods broadly fall into two camps: those summarizing properties of a region and those measuring interactions among regions. Here we pose an unappreciated question in the field: What are the strengths and limitations of each approach to study fundamental neural processes? We explore the relative utility of region- and connection-based measures in the context of three topics of interest: neurobiological relevance, brain-behavior relationships, and individual differences in brain organization. In each section, we offer illustrative examples. We hope that this discussion offers a novel and useful framework to support efforts to better understand the macroscale functional organization of the brain and how it relates to behavior.

Mapping the structural connectivity between the periaqueductal gray and the cerebellum in humans

The periaqueductal gray is a mesencephalic structure involved in modulation of responses to stressful stimuli. Structural connections between the periaqueductal gray and the cerebellum have been described in animals and in a few diffusion tensor imaging studies. Nevertheless, these periaqueductal gray–cerebellum connectivity patterns have yet to be fully investigated in humans. The objective of this study was to qualitatively and quantitatively characterize such pathways using high-resolution, multi-shell data of 100 healthy subjects from the open-access Human Connectome Project repository combined with constrained spherical deconvolution probabilistic tractography. Our analysis revealed robust connectivity density profiles between the periaqueductal gray and cerebellar nuclei, especially with the fastigial nucleus, followed by the interposed and dentate nuclei. High-connectivity densities have been observed between vermal (Vermis IX, Vermis VIIIa, Vermis VIIIb, Vermis VI, Vermis X) and hemispheric cerebellar regions (Lobule IX). Our in vivo study provides for the first time insights on the organization of periaqueductal gray–cerebellar pathways thus opening new perspectives on cognitive, visceral and motor responses to threatening stimuli in humans.

A Distributed Computing Platform for fMRI Big Data Analytics

Since the BRAIN Initiative and Human Brain Project began, a few efforts have been made to address the computational challenges of neuroscience Big Data. The promises of these two projects were to model the complex interaction of brain and behavior and to understand and diagnose brain diseases by collecting and analyzing large quanitites of data. Archiving, analyzing, and sharing the growing neuroimaging datasets posed major challenges. New computational methods and technologies have emerged in the domain of Big Data but have not been fully adapted for use in neuroimaging. In this work, we introduce the current challenges of neuroimaging in a big data context. We review our efforts toward creating a data management system to organize the large-scale fMRI datasets, and present our novel algorithms/methods for the distributed fMRI data processing that employs Hadoop and Spark. Finally, we demonstrate the significant performance gains of our algorithms/methods to perform distributed dictionary learning.

Diffusion MRI in the brain - Theory and concepts

Over the past two decades, diffusion MRI has become an essential tool in neuroimaging investigations. This is due to its sensitivity to the motion of water molecules as they diffuse through the microstructural environment, allowing diffusion MRI to be used as a 'probe' of tissue microstructure. Furthermore, this sensitivity is strongly direction-dependent, notably in brain white matter, due to the alignment of structures that restrict or hinder the motion of water molecules, notably axonal membranes. This provides a means of inferring the orientation of fibres in vivo, and by use of appropriate fibre-tracking algorithms, of delineating the path of white matter tracts in the brain. The ability to perform so-called tractography in humans in vivo non-invasively is unique to diffusion MRI, and is now used in applications such as neurosurgery planning and more broadly within investigations of brain connectomics. This review describes the theory and concepts of diffusion MRI and describes its most important areas of application in the brain, with a strong focus on tractography.

Mapping Structure-Function Relationships in the Brain

Mapping the structural and functional connectivity of the brain is a major focus of systems neuroscience research and will help to identify causally important changes in neural circuitry responsible for behavioral dysfunction. Several methods for examining brain activity in humans have been extended to rodent and monkey models in which molecular and genetic manipulations exist for linking to human disease. In this review, which is part of a special issue focused on bridging brain connectivity information across species and spatiotemporal scales, we address mapping brain activity and neural connectivity in rodents using optogenetics in conjunction with either functional magnetic resonance imaging or optical intrinsic signal imaging. We chose to focus on these techniques because they are capable of reporting spontaneous or evoked hemodynamic activity most closely linked to human neuroimaging studies. We discuss the capabilities and limitations of blood-based imaging methods, usage of optogenetic techniques to map neural systems in rodent models, and other powerful mapping techniques for examining neural connectivity over different spatial and temporal scales. We also discuss implementing strategies for mapping brain connectivity in humans with both basic and clinical applications, and conclude with how cross-species mapping studies can be utilized to influence preclinical imaging studies and clinical practices alike.

Node centrality measures are a poor substitute for causal inference

Network models have become a valuable tool in making sense of a diverse range of social, biological, and information systems. These models marry graph and probability theory to visualize, understand, and interpret variables and their relations as nodes and edges in a graph. Many applications of network models rely on undirected graphs in which the absence of an edge between two nodes encodes conditional independence between the corresponding variables. To gauge the importance of nodes in such a network, various node centrality measures have become widely used, especially in psychology and neuroscience. It is intuitive to interpret nodes with high centrality measures as being important in a causal sense. Using the causal framework based on directed acyclic graphs (DAGs), we show that the relation between causal influence and node centrality measures is not straightforward. In particular, the correlation between causal influence and several node centrality measures is weak, except for eigenvector centrality. Our results provide a cautionary tale: if the underlying real-world system can be modeled as a DAG, but researchers interpret nodes with high centrality as causally important, then this may result in sub-optimal interventions.

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.

A Proposed Mechanism for Spontaneous Transitions between Interictal and Ictal Activity

Epileptic networks are characterized by two outputs: brief interictal spikes and rarer, more prolonged seizures. Although either output state is readily modeled in silico and induced experimentally, the transition mechanisms are unknown, in part because no models exhibit both output states spontaneously. In silico small-world neural networks were built using single-compartment neurons whose physiological parameters were derived from dual whole-cell recordings of pyramidal cells in organotypic hippocampal slice cultures that were generating spontaneous seizure-like activity. In silico, neurons were connected by abundant local synapses and rare long-distance synapses. Activity-dependent synaptic depression and gradual recovery delimited synchronous activity. Full synaptic recovery engendered interictal population spikes that spread via long-distance synapses. When synaptic recovery was incomplete, postsynaptic neurons required coincident activation of multiple presynaptic terminals to reach firing threshold. Only local connections were sufficiently dense to spread activity under these conditions. This coalesced network activity into traveling waves whose velocity varied with synaptic recovery. Seizures were comprised of sustained traveling waves that were similar to those recorded during experimental and human neocortical seizures. Sustained traveling waves occurred only when wave velocity, network dimensions, and the rate of synaptic recovery enabled wave reentry into previously depressed areas at precisely ictogenic levels of synaptic recovery. Wide-field, cellular-resolution GCamP7b calcium imaging demonstrated similar initial patterns of activation in the hippocampus, although the anatomical distribution of traveling waves of synaptic activation was altered by the pattern of synaptic connectivity in the organotypic hippocampal cultures.SIGNIFICANCE STATEMENT When computerized distributed neural network models are required to generate both features of epileptic networks (i.e., spontaneous interictal population spikes and seizures), the network structure is substantially constrained. These constraints provide important new hypotheses regarding the nature of epileptic networks and mechanisms of seizure onset.

[Characteristics of structural and functional organization of the brain]

Current conceptions on the fundamental mechanisms underlying brain functioning in normal conditions and pathological states are considered. The author emphasizes that a great progress in the field has been achieved after the implementation of neuroimaging methods in clinical practice. During the last time, attention is drawn to the connections between separate neurons as well as between different brain regions. Functional specialization and functional integration of different brain regions are key concepts of the higher brain function organization. The significance of the resting state of the brain, which is in fact the active process, is analyzed. The state of cerebral functions determines internal processes in the neuronal tissue. Different aspects of energy metabolism determining the normal functioning of cerebral structures are considered. The expediency of using the drugs influencing the energy metabolism, one of which is 2-ethyl-6-methyl-3-hydroxypyridine succinate (mexidol), is highlighted.

Connectivity Fingerprints: From Areal Descriptions to Abstract Spaces

Fifteen years ago, Passingham and colleagues proposed that brain areas can be described in terms of their unique pattern of input and output connections with the rest of the brain, and that these connections are a crucial determinant of their function. We explore how the advent of neuroimaging of connectivity has allowed us to test and extend this proposal. We show that describing the brain in terms of an abstract connectivity space, as opposed to physical locations of areas, provides a natural and powerful framework for thinking about brain function and its variation across the brains of individuals, populations, and species.

Prenatal stress and the developing brain: Risks for neurodevelopmental disorders

The prenatal period is increasingly considered as a crucial target for the primary prevention of neurodevelopmental and psychiatric disorders. Understanding their pathophysiological mechanisms remains a great challenge. Our review reveals new insights from prenatal brain development research, involving (epi)genetic research, neuroscience, recent imaging techniques, physical modeling, and computational simulation studies. Studies examining the effect of prenatal exposure to maternal distress on offspring brain development, using brain imaging techniques, reveal effects at birth and up into adulthood. Structural and functional changes are observed in several brain regions including the prefrontal, parietal, and temporal lobes, as well as the cerebellum, hippocampus, and amygdala. Furthermore, alterations are seen in functional connectivity of amygdalar-thalamus networks and in intrinsic brain networks, including default mode and attentional networks. The observed changes underlie offspring behavioral, cognitive, emotional development, and susceptibility to neurodevelopmental and psychiatric disorders. It is concluded that used brain measures have not yet been validated with regard to sensitivity, specificity, accuracy, or robustness in predicting neurodevelopmental and psychiatric disorders. Therefore, more prospective long-term longitudinal follow-up studies starting early in pregnancy should be carried out, in order to examine brain developmental measures as mediators in mediating the link between prenatal stress and offspring behavioral, cognitive, and emotional problems and susceptibility for disorders.

Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?

PURPOSE

Human connectomics necessitates high-throughput, whole-brain reconstruction of multiple white matter fiber bundles. Scaling up tractography to meet these high-throughput demands yields new fiber tracking challenges, such as minimizing spurious connections and controlling for gyral biases. The aim of this study is to determine which of the two broadest classes of tractography algorithms-deterministic or probabilistic-is most suited to mapping connectomes.

METHODS

This study develops numerical connectome phantoms that feature realistic network topologies and that are matched to the fiber complexity of in vivo diffusion MRI (dMRI) data. The phantoms are utilized to evaluate the performance of tensor-based and multi-fiber implementations of deterministic and probabilistic tractography.

RESULTS

For connectome phantoms that are representative of the fiber complexity of in vivo dMRI, multi-fiber deterministic tractography yields the most accurate connectome reconstructions (F-measure = 0.35). Probabilistic algorithms are hampered by an abundance of false-positive connections, leading to lower specificity (F = 0.19). While omitting connections with the fewest number of streamlines (thresholding) improves the performance of probabilistic algorithms (F = 0.38), multi-fiber deterministic tractography remains optimal when it benefits from thresholding (F = 0.42).

CONCLUSIONS

Multi-fiber deterministic tractography is well suited to connectome mapping, while connectome thresholding is essential when using probabilistic algorithms.