Short-range connections in the developmental connectome during typical and atypical brain maturation

Functional structure of local connections and differentiation of cerebral cortex areas in the neonate

Neuroimaging research on functional connectivity can provide valuable information on the developmental differentiation of the infant cerebral cortex into its functional areas. We examined healthy neonates to comprehensively map brain functional connectivity using a combination of local measures that uniquely capture the rich spatial structure of cerebral cortex functional connections. Optimal functional MRI scans were obtained in 61 neonates. Local functional connectivity maps were based on Iso-Distance Average Correlation (IDAC) measures. Single distance maps and maps combining three distinct IDAC measures were used to assess different levels of cortical area functional differentiation. A set of brain areas showed higher connectivity than the rest of the brain parenchyma in each local distance map. These areas were consistent with those supporting basic aspects of the neonatal repertoire of adaptive behaviors and included the sensorimotor, auditory and visual cortices, the frontal operculum/anterior insula (relevant for sucking, swallowing and the sense of taste), paracentral lobule (processing anal and urethral sphincter activity), default mode network (relevant for self-awareness), and limbic-emotional structures such as the anterior cingulate cortex, amygdala and hippocampus. However, the results also indicate that brain areas presumed to be actively developing may not necessarily be mature. In fact, combined distance, second-level maps confirmed that the functional differentiation of the cerebral cortex into functional areas in neonates is far from complete. Our results provide a more comprehensive understanding of the developing brain systems, while also highlighting the substantial developmental journey that the neonatal brain must undergo to reach adulthood.

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network

BACKGROUND

Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities.

OBJECTIVE

This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals.

METHODS

In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction.

RESULTS

The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection.

CONCLUSION

The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis.

Abnormal local cortical functional connectivity due to interneuron dysmaturation after neonatal intermittent hypoxia

Background

Premature infants often experience frequent hypoxic episodes due to immaturity of respiratory control that may result in disturbances of gray and white matter development and long-term cognitive and behavioral abnormalities. We hypothesize that neonatal intermittent hypoxia alters cortical maturation of excitatory and inhibitory circuits that can be detected early with functional MRI.

Methods

C57BL/6 mouse pups were exposed to an intermittent hypoxia (IH) regimen consisting of 12 to 20 daily hypoxic episodes of 5% oxygen exposure for 2 min at 37C from P3 to P7, followed by MRI at P12 and electrophysiological recordings in cortical slices and in vivo at several time points between P7 and P13. Behavioral tests were conducted at P41-P50 to assess animal activity and motor learning.

Results

Adult mice after neonatal IH exhibited hyperactivity in open field test and impaired motor learning in complex wheel tasks. Patch clamp and evoked field potential electrophysiology revealed increased glutamatergic transmission accompanied by elevation of tonic inhibition. A decreased synaptic inhibitory drive was evidenced by miniature IPSC frequency on pyramidal cells, multi-unit activity recording in vivo in the motor cortex with selective GABA A receptor inhibitor picrotoxin injection, as well as by the decreased interneuron density at P13. There was also an increased tonic depolarizing effect of picrotoxin after IH on principal cells' membrane potential on patch clamp and direct current potential in extracellular recordings. The amplitude of low-frequency fluctuation on resting-state fMRI was larger, with a larger increase after picrotoxin injection in the IH group.

Conclusions

Increased excitatory glutamatergic transmission, decreased numbers, and activity of inhibitory interneurons after neonatal IH may affect the maturation of connectivity in cortical networks, resulting in long-term cognitive and behavioral changes, including impaired motor learning and hyperactivity. Functional MRI reveals increased intrinsic connectivity in the sensorimotor cortex, suggesting neuronal dysfunction in cortical maturation after neonatal IH. The increased tonic inhibition, presumably due to tonic extrasynaptic GABA receptor drive, may be compensatory to the elevated excitatory glutamatergic transmission.

Altered projection-specific synaptic remodeling and its modification by oxytocin in an idiopathic autism marmoset model

Alterations in the experience-dependent and autonomous elaboration of neural circuits are assumed to underlie autism spectrum disorder (ASD), though it is unclear what synaptic traits are responsible. Here, utilizing a valproic acid-induced ASD marmoset model, which shares common molecular features with idiopathic ASD, we investigate changes in the structural dynamics of tuft dendrites of upper-layer pyramidal neurons and adjacent axons in the dorsomedial prefrontal cortex through two-photon microscopy. In model marmosets, dendritic spine turnover is upregulated, and spines are generated in clusters and survived more often than in control marmosets. Presynaptic boutons in local axons, but not in commissural long-range axons, demonstrate hyperdynamic turnover in model marmosets, suggesting alterations in projection-specific plasticity. Intriguingly, nasal oxytocin administration attenuates clustered spine emergence in model marmosets. Enhanced clustered spine generation, possibly unique to certain presynaptic partners, may be associated with ASD and be a potential therapeutic target.

Investigation of the mechanisms underlying the development and evolution of folds of the cerebrum using gyrencephalic ferrets

The mammalian cerebrum has changed substantially during evolution, characterized by increases in neurons and glial cells and by the expansion and folding of the cerebrum. While these evolutionary alterations are thought to be crucial for acquiring higher cognitive functions, the molecular mechanisms underlying the development and evolution of the mammalian cerebrum remain only partially understood. This is, in part, because of the difficulty in analyzing these mechanisms using mice only. To overcome this limitation, genetic manipulation techniques for the cerebrum of gyrencephalic carnivore ferrets have been developed. Furthermore, successful gene knockout in the ferret cerebrum has been accomplished through the application of the CRISPR/Cas9 system. This review mainly highlights recent research conducted using gyrencephalic carnivore ferrets to investigate the mechanisms underlying the development and evolution of cortical folds.

Distinct subdivisions of subcortical U-fiber regions in the gyrencephalic ferret brain

The human cerebrum contains a large amount of cortico-cortical association fibers. Among them, U-fibers are short-range association fibers located in white matter immediately deep to gray matter. Although U-fibers are thought to be crucial for higher cognitive functions, the organization within U-fiber regions are still unclear. Here we investigated the properties of U-fiber regions in the ferret cerebrum using neurochemical, neuronal tracing, immunohistochemical and electron microscopic techniques. We found that U-fiber regions can be subdivided into two regions, which we named outer and inner U-fiber regions. We further uncovered that outer U-fiber regions have smaller-diameter axons with thinner myelin compared with inner U-fiber regions. These findings may indicate functional complexity within U-fiber regions in the cerebrum.

Frequency-specific functional difference between gyri and sulci in naturalistic paradigm fMRI

Disentangling functional difference between cortical folding patterns of gyri and sulci provides novel insights into the relationship between brain structure and function. Previous studies using resting-state functional magnetic resonance imaging (rsfMRI) have revealed that sulcal signals exhibit stronger high-frequency but weaker low-frequency components compared to gyral ones, suggesting that gyri may serve as functional integration centers while sulci are segregated local processing units. In this study, we utilize naturalistic paradigm fMRI (nfMRI) to explore the functional difference between gyri and sulci as it has proven to record stronger functional integrations compared to rsfMRI. We adopt a convolutional neural network (CNN) to classify gyral and sulcal fMRI signals in the whole brain (the global model) and within functional brain networks (the local models). The frequency-specific difference between gyri and sulci is then inferred from the power spectral density (PSD) profiles of the learned filters in the CNN model. Our experimental results show that nfMRI shows higher gyral-sulcal PSD contrast effect sizes in the global model compared to rsfMRI. In the local models, the effect sizes are either increased or decreased depending on frequency bands and functional complexity of the FBNs. This study highlights the advantages of nfMRI in depicting the functional difference between gyri and sulci, and provides novel insights into unraveling the relationship between brain structure and function.

Improved Functionnectome by dissociating the contributions of white matter fiber classes to functional activation

Integrating the underlying brain circuit's structural and functional architecture is required to explore the functional organization of cognitive networks. In that regard, we recently introduced the Functionnectome. This structural-functional method combines an fMRI acquisition with tractography-derived white matter connectivity data to map cognitive processes onto the white matter. However, this multimodal integration faces three significant challenges: (1) the necessarily limited overlap between tractography streamlines and the grey matter, which may reduce the amount of functional signal associated with the related structural connectivity; (2) the scrambling effect of crossing fibers on functional signal, as a single voxel in such regions can be structurally connected to several cognitive networks with heterogeneous functional signals; and (3) the difficulty of interpretation of the resulting cognitive maps, as crossing and overlapping white matter tracts can obscure the organization of the studied network. In the present study, we tackled these problems by developing a streamline-extension procedure and dividing the white matter anatomical priors between association, commissural, and projection fibers. This approach significantly improved the characterization of the white matter involvement in the studied cognitive processes. The new Functionnectome priors produced are now readily available, and the analysis workflow highlighted here should also be generalizable to other structural-functional approaches. We improved the Functionnectome approach to better study the involvement of white matter in brain function by separating the analysis of the three classes of white matter fibers (association, commissural, and projection fibers). This step successfully clarified the activation maps and increased their statistical significance.

Structural networking of the developing brain: from maturation to neurosurgical implications

Modern neuroscience agrees that neurological processing emerges from the multimodal interaction among multiple cortical and subcortical neuronal hubs, connected at short and long distance by white matter, to form a largely integrated and dynamic network, called the brain "connectome." The final architecture of these circuits results from a complex, continuous, and highly protracted development process of several axonal pathways that constitute the anatomical substrate of neuronal interactions. Awareness of the network organization of the central nervous system is crucial not only to understand the basis of children's neurological development, but also it may be of special interest to improve the quality of neurosurgical treatments of many pediatric diseases. Although there are a flourishing number of neuroimaging studies of the connectome, a comprehensive vision linking this research to neurosurgical practice is still lacking in the current pediatric literature. The goal of this review is to contribute to bridging this gap. In the first part, we summarize the main current knowledge concerning brain network maturation and its involvement in different aspects of normal neurocognitive development as well as in the pathophysiology of specific diseases. The final section is devoted to identifying possible implications of this knowledge in the neurosurgical field, especially in epilepsy and tumor surgery, and to discuss promising perspectives for future investigations.

Virtual lesions in MEG reveal increasing vulnerability of the language network from early childhood through adolescence

In childhood, language outcomes following brain injury are inversely related to age. Neuroimaging findings suggest that extensive representation and/or topological redundancy may confer the pediatric advantage. Here, we assess whole brain and language network resilience using in silico attacks, for 85 children participating in a magnetoencephalography (MEG) study. Nodes are targeted based on eigenvector centrality, betweenness centrality, or at random. The size of each connected component is assessed after iterated node removal; the percolation point, or moment of dis-integration, is defined as the first instance where the second largest component peaks in size. To overcome known effects of fixed thresholding on subsequent graph and resilience analyses, we study percolation across all possible network densities, within a Functional Data Analysis (FDA) framework. We observe age-related increases in vulnerability for random and betweenness centrality-based attacks for whole-brain and stories networks (adjusted-p < 0.05). Here we show that changes in topology underlie increasing language network vulnerability in development.

Preterm-birth alters the development of nodal clustering and neural connection pattern in brain structural network at term-equivalent age

Preterm-born neonates are prone to impaired neurodevelopment that may be associated with disrupted whole-brain structural connectivity. The present study aimed to investigate the longitudinal developmental pattern of the structural network from preterm birth to term-equivalent age (TEA), and identify how prematurity influences the network topological organization and properties of local brain regions. Multi-shell diffusion-weighted MRI of 28 preterm-born scanned a short time after birth (PB-AB) and at TEA (PB-TEA), and 28 matched term-born (TB) neonates in the Developing Human Connectome Project (dHCP) were used to construct structural networks through constrained spherical deconvolution tractography. Structural network development from preterm birth to TEA showed reduced shortest path length, clustering coefficient, and modularity, and more "connector" hubs linking disparate communities. Furthermore, compared with TB newborns, premature birth significantly altered the nodal properties (i.e., clustering coefficient, within-module degree, and participation coefficient) in the limbic/paralimbic, default-mode, and subcortical systems but not global topology at TEA, and we were able to distinguish the PB from TB neonates at TEA based on the nodal properties with 96.43% accuracy. Our findings demonstrated a topological reorganization of the structural network occurs during the perinatal period that may prioritize the optimization of global network organization to form a more efficient architecture; and local topology was more vulnerable to premature birth-related factors than global organization of the structural network, which may underlie the impaired cognition and behavior in PB infants.

Single-subject cortical morphological brain networks across the adult lifespan

Age-related changes in focal cortical morphology have been well documented in previous literature; however, how interregional coordination patterns of the focal cortical morphology reorganize with advancing age is not well established. In this study, we performed a comprehensive analysis of the topological changes in single-subject morphological brain networks across the adult lifespan. Specifically, we constructed four types of single-subject morphological brain networks for 650 participants (aged from 18 to 88 years old), and characterized their topological organization using graph-based network measures. Age-related changes in the network measures were examined via linear, quadratic, and cubic models. We found profound age-related changes in global small-world attributes and efficiency, local nodal centralities, and interregional similarities of the single-subject morphological brain networks. The age-related changes were mainly embodied in cortical thickness networks, involved in frontal regions and highly connected hubs, concentrated on short-range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal, and ventral attention networks. Intriguingly, nonlinear (i.e., quadratic or cubic) age-related changes were frequently found in the insula and limbic regions, and age-related cubic changes preferred long-range morphological connections. Finally, we demonstrated that the morphological similarity in cortical thickness between two frontal regions mediated the relationship between age and cognition measured by Cattell scores. Taken together, these findings deepen our understanding of adaptive changes of the human brain with advancing age, which may account for interindividual variations in behaviors and cognition.

Spatial cell-type enrichment predicts mouse brain connectivity

A fundamental neuroscience topic is the link between the brain's molecular, cellular, and cytoarchitectonic properties and structural connectivity. Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the matrix inversion and subset selection algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell-type densities, demonstrating surprisingly strong prediction accuracy of cell types in general, and particular non-neuronal cells such as oligodendrocytes. We found evidence of a strong distance dependency in the cell connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia were salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.

Mapping alterations in the local synchrony of the cerebral cortex in schizophrenia

BACKGROUND

Observations from different fields of research coincide in indicating that a defective gamma-aminobutyric acid (GABA) interneuron system may be among the primary factors accounting for the varied clinical expression of schizophrenia. GABA interneuron deficiency is locally expressed in the form of neural activity desynchronization. We mapped the functional anatomy of local synchrony in the cerebral cortex in schizophrenia using functional connectivity MRI.

METHODS

Data from 86 patients with schizophrenia and 137 control subjects were obtained from publicly available repositories. Resting-state functional connectivity maps based on Iso-Distant Average Correlation measures across three distances were estimated detailing the local functional structure of the cerebral cortex.

RESULTS

Patients with schizophrenia showed weaker local functional connectivity (i.e., lower MRI signal synchrony) in (i) prefrontal lobe areas, (ii) somatosensory, auditory, visual, and motor cortices, (iii) paralimbic system at the anterior insula and anterior cingulate cortex, and (iv) hippocampus. The distribution of the defect in cortical area synchrony largely coincided with the synchronization effect of the GABA agonist alprazolam previously observed using identical functional connectivity measures. There was also a notable resemblance between the anatomy of our findings and cortical areas showing higher density of parvalbumin (prefrontal lobe and sensory cortices) and somatostatin (anterior insula and anterior cingulate cortex) GABA interneurons in humans.

CONCLUSIONS

Our results thus provide detail of the functional anatomy of synchrony changes in the cerebral cortex in schizophrenia and suggest which elements of the interneuron system are affected. Such information could ultimately be relevant in the search for specific treatments.

Genetic Influence on Gyral Peaks

Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.

New Challenges for Anatomists in the Era of Omics

Anatomic studies have traditionally relied on macroscopic, microscopic, and histological techniques to investigate the structure of tissues and organs. Anatomic studies are essential in many fields, including medicine, biology, and veterinary science. Advances in technology, such as imaging techniques and molecular biology, continue to provide new insights into the anatomy of living organisms. Therefore, anatomy remains an active and important area in the scientific field. The consolidation in recent years of some omics technologies such as genomics, transcriptomics, proteomics, and metabolomics allows for a more complete and detailed understanding of the structure and function of cells, tissues, and organs. These have been joined more recently by "omics" such as radiomics, pathomics, and connectomics, supported by computer-assisted technologies such as neural networks, 3D bioprinting, and artificial intelligence. All these new tools, although some are still in the early stages of development, have the potential to strongly contribute to the macroscopic and microscopic characterization in medicine. For anatomists, it is time to hitch a ride and get on board omics technologies to sail to new frontiers and to explore novel scenarios in anatomy.

Functional network segregation is associated with higher functional connectivity in endurance runners

Neuroimaging studies have identified significant differences in brain structure, function, and connectivity between endurance runners and healthy controls. However, the topological organization of large-scale functional brain networks remains unexplored in endurance runners. Using resting-state functional magnetic resonance imaging data, this study examined the differences in the topological organization of functional networks between endurance runners (n = 22) and healthy controls (n = 20). Endurance runners had significantly higher clustering coefficients in the whole-brain functional network than healthy controls, but the two did not differ regarding the shortest path length or small-world index. Using network-based statistics, we identified one subnetwork in endurance runners with higher functional connectivity than healthy controls, and the mean functional connectivity of the subnetwork significantly correlated with the three aforementioned small-world parameters. In this subnetwork, the mean clustering coefficient of nodes associated with short-range connections was higher in endurance runners than in healthy controls, but the mean clustering coefficient of nodes associated with long-range connections did not differ between the two groups. In conclusion, using graph theoretical approaches, we revealed significant differences in the topological organization of the whole-brain functional network and functional connectivity between endurance runners and healthy controls. The relationship between these two features suggests that a more segregated network may arise from the optimization of the identified subnetwork in endurance runners. These findings are possibly the neural basis underlying the good performance of endurance runners in endurance running.

Examining Conjoint Behavioral Consultation to Support 2e-Autism Spectrum Disorder and Gifted Students in Preschool with Academic and Behavior Concerns

Conjoint behavioral consultation (CBC), as adjusted for 2e children with academic and behavioral difficulties, was the focus of this single-subject design study. Three young children from a preschool participated, together with their parents and teachers. Academic enablers for students-intervention, maintenance, and generalization phases; academic and behavioral competencies-intervention, maintenance, and generalization phases; and teachers', parents', and students' perceptions of the intervention's social validity data were collected among the outcome measures. Findings from multiple participant-related probes pointed to constructive improvements in the phases of intervention, maintenance for listening behavior, and improved on-task skill in intervention, maintenance, and generalization. Additionally, during the consultation, parents and teachers noted improvements in the outcomes of the target behavior such as rhythm keeping, picture-word matching, writing the pictured concept in Turkish, writing the pictured concept in English, short personal story writing, short personal story telling, and verbal math problem solving, and each stakeholder gave the intervention a grade for its social validity. Limitations, potential routes for future study, and implications for preschool CBC intervention are highlighted.

Sources of Heterogeneity in Functional Connectivity During English Word Processing in Bilingual and Monolingual Children

Diversity and variation in language experiences, such as bilingualism, contribute to heterogeneity in children's neural organization for language and brain development. To uncover sources of such heterogeneity in children's neural language networks, the present study examined the effects of bilingual proficiency on children's neural organization for language function. To do so, we took an innovative person-specific analytical approach to investigate young Chinese-English and Spanish-English bilingual learners of structurally distinct languages. Bilingual and English monolingual children (N = 152, M(SD)_age = 7.71(1.32)) completed an English word recognition task during functional near-infrared spectroscopy neuroimaging, along with language and literacy tasks in each of their languages. Two key findings emerged. First, bilinguals' heritage language proficiency (Chinese or Spanish) made a unique contribution to children's language network density. Second, the findings reveal common and unique patterns in children's patterns of task-related functional connectivity. Common across all participants were short-distance neural connections within left hemisphere regions associated with semantic processes (within middle temporal and frontal regions). Unique to more proficient language users were additional long-distance connections between frontal, temporal, and bilateral regions within the broader language network. The study informs neurodevelopmental theories of language by revealing the effects of heterogeneity in language proficiency and experiences on the structure and quality of emerging language neural networks in linguistically diverse learners.

Age related changes and sex related differences of functional brain networks in childhood: A high-density EEG study

OBJECTIVE

The aim of this study was to explore functional network age-related changes and sex-related differences during the early lifespan with a high-density resting state electroencephalography (rs-EEG).

METHODS

We analyzed two data sets of high-density rs-EEG in healthy children and adolescents. We recorded a 64-channel EEG and calculated functional connectomes in 27 participants aged 5-18 years. To validate our results, we used publicly available data and calculated functional connectomes in another 86 participants aged 6-18 years from a 128-channel rs-EEG. We were primarily interested in alpha frequency band, but we also analyzed theta and beta frequency bands.

RESULTS

We observed age-related increase of characteristic path, clustering coefficient and interhemispheric strength in the alpha frequency band of both data sets and in the beta frequency band of the larger validation data set. Age-related increase of global efficiency was seen in the theta band of the validation data set and in the alpha band of the test data set. Increase in small worldness was observed only in the alpha frequency band of the test data set. We also observed an increase of individual peak alpha frequency with age in both data sets. Sex-related differences were only observed in the beta frequency band of the larger validation data set, with females having higher values than same aged males.

CONCLUSIONS

Functional brain networks show indices of higher segregation, but also increasing global integration with maturation. Age-related changes are most prominent in the alpha frequency band.

SIGNIFICANCE

To the best of our knowledge, our study was the first to analyze maturation related changes and sex-related differences of functional brain networks with a high-density EEG and to compare functional connectomes generated from two diverse high-density EEG data sets. Understanding the age-related changes and sex-related differences of functional brain networks in healthy children and adolescents is crucial for identifying network abnormalities in different neurologic and psychiatric conditions, with the aim to identify possible markers for prognosis and treatment.

Aberrant frontal lobe "U"-shaped association fibers in first-episode schizophrenia: A 7-Tesla Diffusion Imaging Study

Schizophrenia is believed to be a developmental disorder with one hypothesis suggesting that symptoms arise due to abnormal interactions (or disconnectivity) between different brain regions. While some major deep white matter pathways have been extensively studied (e.g. arcuate fasciculus), studies of short-ranged, "U"-shaped tracts have been limited in patients with schizophrenia, in part due to the sheer abundance of tracts present and due to the spatial variations across individuals that defy probabilistic characterization in the absence of reliable templates. In this study, we use diffusion magnetic resonance imaging (dMRI) to investigate frontal lobe superficial white matter that are present in the majority of study participants, comparing healthy controls and minimally treated patients with first-episode schizophrenia (<3 median days of lifetime treatment). Through group comparisons, 3 out of 63 frontal lobe "U"-shaped tracts were found to demonstrate localized aberrations affecting the microstructural tissue properties (via diffusion tensor metrics) in this early stage of disease. No associations were found in patients between aberrant segments of affected tracts and clinical or cognitive variables. Aberrations in the frontal lobe "U"-shaped tracts in early untreated stages of psychosis occur irrespective of symptom burden, and are distributed across critical functional networks associated with executive function and salience processing. While we limited the investigation to the frontal lobe, a framework has been developed to study such connections in other brain regions, enabling further extensive investigations jointly with the major deep white matter pathways.

The sleeping brain's connectivity and family environment: characterizing sleep EEG coherence in an infant cohort

Brain connectivity closely reflects brain function and behavior. Sleep EEG coherence, a measure of brain's connectivity during sleep, undergoes pronounced changes across development under the influence of environmental factors. Yet, the determinants of the developing brain's sleep EEG coherence from the child's family environment remain unknown. After characterizing high-density sleep EEG coherence in 31 healthy 6-month-old infants by detecting strongly synchronized clusters through a data-driven approach, we examined the association of sleep EEG coherence from these clusters with factors from the infant's family environment. Clusters with greatest coherence were observed over the frontal lobe. Higher delta coherence over the left frontal cortex was found in infants sleeping in their parents' room, while infants sleeping in a room shared with their sibling(s) showed greater delta coherence over the central parts of the frontal cortex, suggesting a link between local brain connectivity and co-sleeping. Finally, lower occipital delta coherence was associated with maternal anxiety regarding their infant's sleep. These interesting links between sleep EEG coherence and family factors have the potential to serve in early health interventions as a new set of targets from the child's immediate environment.

CDKL5 sculpts functional callosal connectivity to promote cognitive flexibility

Functional and structural connectivity alterations in short- and long-range projections have been reported across neurodevelopmental disorders (NDD). Interhemispheric callosal projection neurons (CPN) represent one of the major long-range projections in the brain, which are particularly important for higher-order cognitive function and flexibility. However, whether a causal relationship exists between interhemispheric connectivity alterations and cognitive deficits in NDD remains elusive. Here, we focused on CDKL5 Deficiency Disorder (CDD), a severe neurodevelopmental disorder caused by mutations in the X-linked Cyclin-dependent kinase-like 5 (CDKL5) gene. We found an increase in homotopic interhemispheric connectivity and functional hyperconnectivity across higher cognitive areas in adult male and female CDKL5-deficient mice by resting-state functional MRI (rs-fMRI) analysis. This was accompanied by an increase in the number of callosal synaptic inputs but decrease in local synaptic connectivity in the cingulate cortex of juvenile CDKL5-deficient mice, suggesting an impairment in excitatory synapse development and a differential role of CDKL5 across excitatory neuron subtypes. These deficits were associated with significant cognitive impairments in CDKL5 KO mice. Selective deletion of CDKL5 in the largest subtype of CPN likewise resulted in an increase of functional callosal inputs, without however significantly altering intracortical cingulate networks. Notably, such callosal-specific changes were sufficient to cause cognitive deficits. Finally, when CDKL5 was selectively re-expressed only in this CPN subtype, in otherwise CDKL5-deficient mice, it was sufficient to prevent the cognitive impairments of CDKL5 mutants. Together, these results reveal a novel role of CDKL5 by demonstrating that it is both necessary and sufficient for proper CPN connectivity and cognitive function and flexibility, and further validates a causal relationship between CPN dysfunction and cognitive impairment in a model of NDD.

Gerstmann Syndrome as a Disconnection Syndrome: A Single Case Diffusion Tensor Imaging Study

Gerstmann syndrome (GS) is a rare syndrome that occurs when there is a lesion of the dominant inferior parietal lobule (IPL), causing agraphia, acalculia, finger agnosia, and right-left disorientation. A 49-year-old right-handed male was diagnosed as GS after left parieto-occipital lobe hemorrhage. The patient showed mild anomic aphasia with agraphia in the language test and the neuropsychological test revealed acalculia, impaired right-left discrimination, and finger agnosia. In diffusion tensor tractography, the tracts of left superior longitudinal fasciculus (SLF), middle longitudinal fasciculus, U-fibers and posterior corpus callosum (CC) were disrupted around the left IPL. In addition, fractional anisotropy (FA) values were markedly decreased in left SLF, and posterior CC when compared to twelve healthy control subjects. Our clinical and neuroimaging findings support that GS is a disconnection syndrome caused by lesion in the white matter pathway surrounding IPL. In future, more studies of the correlation between the white matter disconnection and the development of GS including high quality imaging technique are needed.

The different damage patterns of short-, middle- and long-range connections between patients with relapse-remitting multiple sclerosis and neuromyelitis optica spectrum disorder

Objective

To investigate the differences in short-, middle- and long-range connections between patients with relapse-remitting multiple sclerosis (RRMS) and neuromyelitis optica spectrum disorder (NMOSD), and their correlation with brain tissue volume, structural and functional network parameters.

Methods

A total of 51 RRMS, 42 NMOSD and 56 health controls (HC) were recruited. Of these 25 RRMS (median: 1.37 years) and 20 NMOSD (median: 1.25 years) patients were also studied at follow-up. The whole-brain fiber connection was divided into three groups according to the trisected lengths of the tract in HC group, including short-, middle- and long-range connections. The brain tissue features (including total brain tissue and deep grey matter volumes) and parameters of DTI and functional networks (including the shortest path, clustering coefficient, local efficiency and global efficiency) were calculated. The differences in fiber number (FN) and average fractional anisotropy (FA) were compared between RRMS and NMOSD by the One-way ANOVA and post hoc tests. The correlation between the FN or FA and the brain tissue volume, DTI and functional network parameters were further analyzed by Pearson analysis.

Results

Compared to HC and NMOSD, the total number of fibers in RRMS was decreased, including the reduced FN of middle- and long-range connections, but increased FN of short-range connections. Compared to HC, the FA of three fibers in RRMS and NMOSD were reduced significantly, and the decrease of FA in RRMS was greater than in NMOSD. There were correlations between the FN of short-, and long-range connections and the atrophy of whole brain tissue in two diseases and structural network topological parameters in RRMS. Additionally, there was no significant difference of FN and FA in short-, middle- and long-range connections between the baseline and follow-up in two diseases.

Conclusions

RRMS and NMOSD patients have different patterns of fiber connection damage. The FN of different lengths in RRMS and NMOSD patients may be associated with brain atrophy. The FN and FA of different lengths may explain the decreased efficiency of the structural network in RRMS patients. In the short-term follow-up, neither has worsened damage of different fibers in two diseases.

Associations between maternal prenatal depression and neonatal behavior and brain function - Evidence from the functional near-infrared spectroscopy

BACKGROUND

Maternal prenatal depression is a significant public health issue associated with mental disorders of offspring. This study aimed to determine if maternal prenatal depressive symptoms are associated with changes in neonatal behaviors and brain function at the resting state.

METHODS

A total of 204 pregnant women were recruited during the third trimester and were evaluated by Edinburgh Postpartum Depression Scale (EPDS). The mother-infant pairs were divided into the depressed group (n = 75) and control group (n = 129) based on the EPDS, using a cut-off value of 10. Cortisol levels in the cord blood and maternal blood collected on admission for delivery were measured. On day three of life, all study newborns were evaluated by the Neonatal Behavior Assessment Scale (NBAS) and 165 infants were evaluated by resting-state functional near-infrared spectroscopy (rs-fNIRS). To minimize the influences of potential bias on the rs-fNIRS results, we used a binary logistic regression model to carry out propensity score matching between the depressed group and the control group. Rs-fNIRS data from 21 pairs of propensity score-matched newborns were used for analysis. The associations between maternal EPDS scores, neonatal NBAS scores, and cortisol levels were analyzed using linear regressions and the mediation analysis models.

RESULTS

Compared to the control group, the newborns in the depressed group had lower scores in the social-interaction and autonomic system dimensions of NBAS (P < 0.01). Maternal and umbilical cord plasma cortisol levels in the depressed group were higher (P < 0.01) than in the control group. However, only umbilical cord plasma cortisol played a negative mediating role in the relationship between maternal EPDS and NBAS in the social-interaction and autonomic system (β med = -0.054 [-0.115,-0.018] and -0.052 [-0.105,-0.019]. Proportional mediation was 13.57 % and 12.33 for social-interaction and autonomic systems, respectively. The newborns in the depressed group showed decreases in the strength of rs-fNIRS functional connections, primarily the connectivity of the left frontal-parietal and temporal-parietal regions. However, infants in the depressed and control groups showed no differences in topological characteristics of the brain network, including standardized clustering coefficient, characteristic path length, small-world property, global efficiency, and local efficiency (P > 0.05). The social-interaction Z-scores had positive correlations with functional connectivity strength of left prefrontal cortex-left parietal lobe (r = 0.57, p < 0.01)，prefrontal cortex-left parietal lobe - left temporal lobe (r = 0.593, p < 0.01) and left parietal lobe - left temporal lobe (r = 0.498, p < 0.01). Autonomic system Z-scores were also significantly positive correlation with prefrontal cortex-left parietal lobe (r = 0.509, p < 0.01)，prefrontal cortex-left parietal lobe - left temporal lobe (r = 0.464, p < 0.01), left parietal lobe - left temporal lobe (r = 0.381, p < 0.05), and right temporal lobe and left temporal lobe (r = 0.310, p < 0.05).

CONCLUSION

This study shows that maternal prenatal depression may affect the development of neonatal social-interaction and autonomic system and the strength of neonatal brain functional connectivity. The fetal cortisol may play a role in behavioral development in infants exposed to maternal prenatal depression. Our findings highlight the importance of prenatal screening for maternal depression and early postnatal behavioral evaluation that provide the opportunity for early diagnosis and intervention to improve neurodevelopmental outcomes.

Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Altered neural mechanism of social reward anticipation in individuals with schizophrenia and social anhedonia

Altered social reward anticipation could be found in schizophrenia (SCZ) patients and individuals with high levels of social anhedonia (SA). However, few research investigated the putative neural processing for altered social reward anticipation in these populations on the SCZ spectrum. This study aimed to examine the underlying neural mechanisms of social reward anticipation in these populations. Twenty-three SCZ patients and 17 healthy controls (HC), 37 SA individuals and 50 respective HCs completed the Social Incentive Delay (SID) imaging task while they were undertaking MRI brain scans. We used the group contrast to examine the alterations of BOLD activation and functional connectivity (FC, psychophysiological interactions analysis). We then characterized the beta-series social brain network (SBN) based on the meta-analysis results from NeuroSynth and examined their prediction effects on real-life social network (SN) characteristics using the partial least squared regression analysis. The results showed that SCZ patients exhibited hypo-activation of the left medial frontal gyrus and the negative FCs with the left parietal regions, while individuals with SA showed the hyper-activation of the left middle frontal gyrus when anticipating social reward. For the beta-series SBNs, SCZ patients had strengthened cerebellum-temporal FCs, while SA individuals had strengthened left frontal regions FCs. However, such FCs of the SBN failed to predict the real-life SN characteristics. These preliminary findings suggested that SCZ patients and SA individuals appear to exhibit altered neural processing for social reward anticipation, and such neural activities showed a weakened association with real-life SN characteristics.

Surface-based tracking for short association fibre tractography

It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.

Wide Dissection Trans-Sulcal Approach for Resection of Deep Intra-Axial Lesions in Eloquent Brain Areas

Introduction: Resection of intra-axial tumors (IaT) in eloquent brain regions risks major postoperative neurological deficits. Awake craniotomy is often used to navigate these areas; however, some patients are ineligible for awake procedures. The trans-sulcal approach (TScal) was introduced to reduce parenchymal trauma during tumor resection. We report our experiences utilizing TScal for resection of deep IaT located in eloquent areas. Materials and Methods: This is a single-center retrospective analysis of patients who underwent IaT resection in eloquent areas via TScal from January 2013 to April 2021. Seventeen cases were reviewed, and relevant data was collected. Fluorescence-guided surgery with 5-aminolevulinic acid (ALA) and intraoperative ultrasound was performed in some cases. Results: Seventeen patients (10 males, 7 females) averaging 61.2 years-old (range, 21-76) were included in this study. Average length of stay was 4.8 days, and only 2 patients (11.8%) required hospital readmission within 30 days. Gross total resection (GTR) was achieved in 15 patients (88.2%), while subtotal resection occurred in 2 patients (11.8%). Eleven patients (64.7%) reported full resolution of symptoms, 4 patients (23.5%) reported deficit improvement, and 2 patients (11.8%) experienced no change from their preoperative deficits. No patient developed new permanent deficits postoperatively. Discussion: GTR, preoperative deficit reduction, and complications were comparable to awake craniotomy and other TScal studies. Ancillary intraoperative techniques, such as brain mapping, 5-ALA and intraoperative ultrasound, are afforded by TScal to improve resection rates and overall outcomes. Conclusions: TScal can be an option for patients with deep lesions in eloquent areas who are not candidates for awake surgeries.

A computational model of inner speech supporting flexible goal-directed behaviour in Autism

Experimental and computational studies propose that inner speech boosts categorisation skills and executive functions, making human behaviour more focused and flexible. In addition, many clinical studies highlight a relationship between poor inner-speech and an executive impairment in autism spectrum condition (ASC), but contrasting findings are reported. Here we directly investigate the latter issue through a previously implemented and validated computational model of the Wisconsin Cards Sorting Tests. In particular, the model was applied to explore potential individual differences in cognitive flexibility and inner speech contribution in autistic and neurotypical participants. Our model predicts that the use of inner-speech could increase along the life-span of neurotypical participants but would be reduced in autistic ones. Although we found more attentional failures (i.e., wrong behavioural rule switches) in autistic children/teenagers and more perseverative behaviours in autistic young/older adults, only autistic children and older adults exhibited a lower performance (i.e., fewer consecutive correct rule switches) than matched control groups. Overall, our results corroborate the idea that the reduced use of inner speech could represent a disadvantage for autistic children and autistic older adults. Moreover, the results suggest that cognitive-behavioural therapies should focus on developing inner speech skills in autistic children as this could provide cognitive support throughout their whole life span.

Investigation of the Mechanisms Underlying the Development and Evolution of the Cerebral Cortex Using Gyrencephalic Ferrets

The mammalian cerebral cortex has changed significantly during evolution. As a result of the increase in the number of neurons and glial cells in the cerebral cortex, its size has markedly expanded. Moreover, folds, called gyri and sulci, appeared on its surface, and its neuronal circuits have become much more complicated. Although these changes during evolution are considered to have been crucial for the acquisition of higher brain functions, the mechanisms underlying the development and evolution of the cerebral cortex of mammals are still unclear. This is, at least partially, because it is difficult to investigate these mechanisms using mice only. Therefore, genetic manipulation techniques for the cerebral cortex of gyrencephalic carnivore ferrets were developed recently. Furthermore, gene knockout was achieved in the ferret cerebral cortex using the CRISPR/Cas9 system. These techniques enabled molecular investigations using the ferret cerebral cortex. In this review, we will summarize recent findings regarding the mechanisms underlying the development and evolution of the mammalian cerebral cortex, mainly focusing on research using ferrets.

Short-Range Structural Connections Are More Severely Damaged in Early-Stage MS

BACKGROUND AND PURPOSE

Long-range connections are more severely damaged and relevant for cognition in long-standing MS. However, the evolution of such coordinated network damage in patients with MS is unclear. We investigated whether short- and long-range structural connections sustained equal damage in early-stage MS.

MATERIALS AND METHODS

Sixteen patients with early-stage MS and 17 healthy controls were scanned by high-resolution, multishell diffusion imaging on 7T MR imaging and assessed cognitively. We investigated macrostructural properties in short- and long-range fibers and of microstructural metrics derived from 2 quantitative diffusion MR imaging models: DTI and neurite orientation dispersion and density imaging.

RESULTS

Patients had significant WM integrity damage-that is, higher radial diffusivity and a lower intracellular volume fraction in the focal WM lesions. Compared with the healthy controls, the patients had noticeable microstructure changes in both short- and long-range fibers, including increased radial diffusivity, mean diffusivity, and axial diffusivity. Z scores further indicated greater damage in the short-range fibers than in the long-range fibers.

CONCLUSIONS

Our findings demonstrate that more severe demyelination preceding axonal degeneration occurs in short-range connections but not in long-range connections in early-stage MS, suggesting the possibility that there are cortical lesions that are undetectable by current MR imaging.

Flattened Structural Network Changes and Association of Hyperconnectivity With Symptom Severity in 2-7-Year-Old Children With Autism

Understanding the brain differences present at the earliest possible diagnostic age for autism spectrum disorder (ASD) is crucial for delineating the underlying neuropathology of the disorder. However, knowledge of brain structural network changes in the early important developmental period between 2 and 7 years of age is limited in children with ASD. In this study, we aimed to fill the knowledge gap by characterizing age-related brain structural network changes in ASD from 2 to 7 years of age, and identify sensitive network-based imaging biomarkers that are significantly correlated with the symptom severity. Diffusion MRI was acquired in 30 children with ASD and 21 typically developmental (TD) children. With diffusion MRI and quantified clinical assessment, we conducted network-based analysis and correlation between graph-theory-based measurements and symptom severity. Significant age-by-group interaction was found in global network measures and nodal efficiencies during the developmental period of 2-7 years old. Compared with significant age-related growth of the structural network in TD, relatively flattened maturational trends were observed in ASD. Hyper-connectivity in the structural network with higher global efficiency, global network strength, and nodal efficiency were observed in children with ASD. Network edge strength in ASD also demonstrated hyper-connectivity in widespread anatomical connections, including those in default-mode, frontoparietal, and sensorimotor networks. Importantly, identified higher nodal efficiencies and higher network edge strengths were significantly correlated with symptom severity in ASD. Collectively, structural networks in ASD during this early developmental period of 2-7 years of age are characterized by hyper-connectivity and slower maturation, with aberrant hyper-connectivity significantly correlated with symptom severity. These aberrant network measures may serve as imaging biomarkers for ASD from 2 to 7 years of age.

The importance of basal-temporal white matter to pre- and post-surgical naming ability in temporal lobe epilepsy

OBJECTIVE

Emerging research highlights the importance of basal-temporal cortex, centered on the fusiform gyrus, to both pre-surgical naming ability and post-surgical naming outcomes in temporal lobe epilepsy (TLE). In this study, we investigate whether integrity of the white matter network that interconnects this basal region to the distributed language network affects naming ability and risk for post-surgical naming decline.

METHODS

Patients with drug-resistant TLE were recruited from two epilepsy centers in a prospective longitudinal study. The pre-surgical dataset included 50 healthy controls, 47 left TLE (L-TLE), and 41 right TLE (R-TLE) patients. All participants completed pre-surgical T1- and diffusion-weighted MRI (dMRI), as well as neuropsychological tests of auditory and visual naming. Nineteen L-TLE and 18 R-TLE patients underwent anterior temporal lobectomy (ATL) and also completed post-surgical neuropsychological testing. Pre-surgical fractional anisotropy (FA) of the white matter directly beneath the fusiform neocortex (i.e., superficial white matter; SWM) and of deep white matter tracts with connections to the basal-temporal cortex [inferior longitudinal fasciculus (ILF) and inferior frontal occipital fasciculus (IFOF)] was calculated. Clinical variables, hippocampal volume, and FA of each white matter tract or region were examined in linear regressions with naming scores, or change in naming scores, as the primary outcomes.

RESULTS

Pre-surgically, higher FA in the bilateral ILF, bilateral IFOF, and left fusiform SWM was associated with better visual and auditory naming scores (all ps < 0.05 with FDR correction). In L-TLE, higher pre-surgical FA was also associated with less naming decline post-surgically, but results varied across tracts. When including only patients with typical language dominance, only integrity of the right fusiform SWM was associated with less visual naming decline (p = .0018).

DISCUSSION

Although a broad network of white matter network matter may contribute to naming ability pre-surgically, the reserve capacity of the contralateral (right) fusiform SWM may be important for mitigating visual naming decline following ATL in L-TLE. This shows that the study of the structural network interconnecting the basal-temporal region to the wider language network has implications for understanding both pre- and post-surgical naming in TLE.

Right Anterior Theta Hypersynchrony as a Quantitative Measure Associated with Autistic Traits and K-Cl Cotransporter KCC2 Polymorphism

Our aim was to use theta coherence as a quantitative trait to investigate the relation of the polymorphisms in NKCC1 (rs3087889) and KCC2 (rs9074) channel protein genes to autistic traits (AQ) in neurotypicals. Coherence values for candidate connection regions were calculated from eyes-closed resting EEGs in two independent groups. Hypersynchrony within the right anterior region was related to AQ in both groups (p < 0.05), and variability in this hypersynchrony was related to the rs9074 polymorphism in the total group (p < 0.05). In conclusion, theta hypersynchrony within the right anterior region during eyes-closed rest can be considered a quantitative measure for autistic traits. Replicating our findings in two independent populations with different backgrounds strengthens the validity of the current study.

Loss and dispersion of superficial white matter in Alzheimer's disease: a diffusion MRI study

Pathological cerebral white matter changes in Alzheimer's disease have been shown using diffusion tensor imaging. Superficial white matter changes are relatively understudied despite their importance in cortico-cortical connections. Measuring superficial white matter degeneration using diffusion tensor imaging is challenging due to its complex organizational structure and proximity to the cortex. To overcome this, we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are degenerative (e.g. loss of myelinated fibres) and organizational (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, grey/white boundary, superficial white matter (1 mm below grey/white boundary) and superficial/deeper white matter (2 mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The superficial white matter of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P < 0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P < 0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the entorhinal and parahippocampus regions (both P < 0.05) and higher fractional anisotropy within the postcentral region (P < 0.05). Mean diffusivity was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P < 0.05) and lower in the postcentral, precentral and superior temporal regions (all P < 0.05). In the overlying grey matter, disease-related changes were largely consistent with superficial white matter findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P < 0.001) but group differences reduced in magnitude and coverage when moving towards the superficial white matter. These results show that microstructural changes occur within superficial white matter and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying fibres undergo neurodegeneration and organizational changes, two effects previously indiscernible using standard diffusion tensor metrics in superficial white matter.

Mapping covariance in brain FDG uptake to structural connectivity

PURPOSE

Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDG_cov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDG_cov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDG_cov and structural connectivity networks.

METHODS

We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDG_cov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking.

RESULTS

For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDG_cov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections.

CONCLUSION

Structural connectivity via white matter fiber tracts is a relevant substrate of FDG_cov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDG_cov connections.

Individual Uniqueness in the Neonatal Functional Connectome

The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0-30 mm) and middle-range (30-60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses

Premature birth affects the developmental trajectory of the brain during a period of intense maturation with possible lifelong consequences. To better understand the effect of prematurity on brain structure and function, we performed blood‐oxygen‐level dependent (BOLD) and anatomical magnetic resonance imaging (MRI) at 40 weeks of postmenstrual age on 88 newborns with variable gestational age (GA) at birth and no evident radiological alterations. We extracted measures of resting‐state functional connectivity and activity in a set of 90 cortical and subcortical brain regions through the evaluation of BOLD correlations between regions and of fractional amplitude of low‐frequency fluctuation (fALFF) within regions, respectively. Anatomical information was acquired through the assessment of regional volumes. We performed univariate analyses on each metric to examine the association with GA at birth, the spatial distribution of the effects, and the consistency across metrics. Moreover, a data‐driven multivariate analysis (i.e., Machine Learning) framework exploited the high dimensionality of the data to assess the sensitivity of each metric to the effect of premature birth. Prematurity was associated with bidirectional alterations of functional connectivity and regional volume and, to a lesser extent, of fALFF. Notably, the effects of prematurity on functional connectivity were spatially diffuse, mainly within cortical regions, whereas effects on regional volume and fALFF were more focal, involving subcortical structures. While the two analytical approaches delivered consistent results, the multivariate analysis was more sensitive in capturing the complex pattern of prematurity effects. Future studies might apply multivariate frameworks to identify premature infants at risk of a negative neurodevelopmental outcome.

Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants

Significance: Early monolingual versus bilingual experience induces adaptations in the development of linguistic and cognitive processes, and it modulates functional activation patterns during the first months of life. Resting-state functional connectivity (RSFC) is a convenient approach to study the functional organization of the infant brain. RSFC can be measured in infants during natural sleep, and it allows to simultaneously investigate various functional systems. Adaptations have been observed in RSFC due to a lifelong bilingual experience. Investigating whether bilingualism-induced adaptations in RSFC begin to emerge early in development has important implications for our understanding of how the infant brain's organization can be shaped by early environmental factors. Aims: We attempt to describe RSFC using functional near-infrared spectroscopy (fNIRS) and to examine whether it adapts to early monolingual versus bilingual environments. We also present an fNIRS data preprocessing and analysis pipeline that can be used to reliably characterize RSFC in development and to reduce false positives and flawed results interpretations. Methods: We measured spontaneous hemodynamic brain activity in a large cohort ( N = 99 ) of 4-month-old monolingual and bilingual infants using fNIRS. We implemented group-level approaches based on independent component analysis to examine RSFC, while providing proper control for physiological confounds and multiple comparisons. Results: At the group level, we describe the functional organization of the 4-month-old infant brain in large-scale cortical networks. Unbiased group-level comparisons revealed no differences in RSFC between monolingual and bilingual infants at this age. Conclusions: High-quality fNIRS data provide a means to reliably describe RSFC patterns in the infant brain. The proposed group-level RSFC analyses allow to assess differences in RSFC across experimental conditions. An effect of early bilingual experience in RSFC was not observed, suggesting that adaptations might only emerge during explicit linguistic tasks, or at a later point in development.

Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

Folding of the cerebral cortex is a prominent characteristic of mammalian brains. Alterations or deficits in cortical folding are strongly correlated with abnormal brain function, cognition, and behavior. Therefore, a precise mapping between the anatomy and function of the brain is critical to our understanding of the mechanisms of brain structural architecture in both health and diseases. Gyri and sulci, the standard nomenclature for cortical anatomy, serve as building blocks to make up complex folding patterns, providing a window to decipher cortical anatomy and its relation with brain functions. Huge efforts have been devoted to this research topic from a variety of disciplines including genetics, cell biology, anatomy, neuroimaging, and neurology, as well as involving computational approaches based on machine learning and artificial intelligence algorithms. However, despite increasing progress, our understanding of the functional anatomy of gyro-sulcal patterns is still in its infancy. In this review, we present the current state of this field and provide our perspectives of the methodologies and conclusions concerning functional differentiation between gyri and sulci, as well as the supporting information from genetic, cell biology, and brain structure research. In particular, we will further present a proposed framework for attempting to interpret the dynamic mechanisms of the functional interplay between gyri and sulci. Hopefully, this review will provide a comprehensive summary of anatomo-functional relationships in the cortical gyro-sulcal system together with a consideration of how these contribute to brain function, cognition, and behavior, as well as to mental disorders.

Transient structural MRI patterns correlate with the motor functions in preterm infants

AIM

To explore the relationships between transient structural brain patterns on MRI at preterm and at term-equivalent age (TEA) as a predictor of general movements (GMs) and motor development at 1-year corrected age (CA) in very preterm infants.

METHODS

In this prospective study, 30 very preterm infants (median = 28wks; 16 males) had structural magnetic resonance imaging (MRI) at preterm (median = 31wks + 6d) and at TEA (median = 40wks) and neuromotor assessments. The quality of GMs was assessed by Prechtl's general movements assessment and a detailed analysis of the motor repertoire was performed by calculating a motor optimality score (MOS), both at term age and at 3 months post-term. Motor development at 1-year CA was evaluated with the Infant Motor Profile (IMP). Associations between qualitative MRI findings and neuromotor scores were investigated.

RESULTS

Abnormal GMs and low motor performance at 1-year CA were associated with the poor visibility of transient structural pattern, that is with sagittal strata.

INTERPRETATION

Transient structural MRI pattern, sagittal strata, at preterm age is related to the quality of GMs and later motor development in preterm infants. This transient fetal brain compartment may be considered as a component of neurobiological basis for early neuromotor behavior, as expressed by GMs.

Behavior and Regional Cortical BOLD Signal Fluctuations Are Altered in Adult Rabbits After Neonatal Volatile Anesthetic Exposure

Neonatal and infant exposure to volatile anesthetics has been associated with long-term learning, memory, and behavioral deficits. Although early anesthesia exposure has been linked to a number of underlying structural abnormalities, functional changes associated with these impairments remain poorly understood. To investigate the relationship between functional alteration in neuronal circuits and learning deficiency, resting state functional MRI (rsfMRI) connectivity was examined in adolescent rabbits exposed to general anesthesia as neonates (1 MAC isoflurane for 2 h on postnatal days P8, P11, and P14) and unanesthetized controls before and after training with a trace eyeblink classical conditioning (ECC) paradigm. Long-range connectivity was measured between several key regions of interest (ROIs), including primary and secondary somatosensory cortices, thalamus, hippocampus, and cingulate. In addition, metrics of regional BOLD fluctuation amplitudes and coherence, amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) were calculated. Our results showed that the trace ECC learning rate was significantly lower in the anesthesia-exposed group. No anesthesia-related changes in long-range connectivity, fALFF, or ReHo were found between any ROIs. However, ALFF was significantly higher in anesthesia-exposed rabbits in the primary and secondary somatosensory cortices, and ALFF in those areas was a significant predictor of the learning performance for trace ECC. The absence of anesthesia-related changes in long-range thalamocortical connectivity indicates that functional thalamocortical input is not affected. Higher ALFF in the somatosensory cortex may indicate the developmental disruption of cortical neuronal circuits after neonatal anesthesia exposure, including excessive neuronal synchronization that may underlie the observed cognitive deficits.

Atypical measures of diffusion at the gray-white matter boundary in autism spectrum disorder in adulthood

Autism spectrum disorder (ASD) is a highly complex neurodevelopmental condition that is accompanied by neuroanatomical differences on the macroscopic and microscopic level. Findings from histological, genetic, and more recently in vivo neuroimaging studies converge in suggesting that neuroanatomical abnormalities, specifically around the gray‐white matter (GWM) boundary, represent a crucial feature of ASD. However, no research has yet characterized the GWM boundary in ASD based on measures of diffusion. Here, we registered diffusion tensor imaging data to the structural T1‐weighted images of 92 adults with ASD and 92 matched neurotypical controls in order to examine between‐group differences and group‐by‐sex interactions in fractional anisotropy and mean diffusivity sampled at the GWM boundary, and at different sampling depths within the superficial white and into the gray matter. As hypothesized, we observed atypical diffusion at and around the GWM boundary in ASD, with between‐group differences and group‐by‐sex interactions depending on tissue class and sampling depth. Furthermore, we identified that altered diffusion at the GWM boundary partially (i.e., ~50%) overlapped with atypical gray‐white matter tissue contrast in ASD. Our study thus replicates and extends previous work highlighting the GWM boundary as a crucial target of neuropathology in ASD, and guides future work elucidating etiological mechanisms.

Interhemispheric Relationship of Genetic Influence on Human Brain Connectivity

To understand the origins of interhemispheric differences and commonalities/coupling in human brain wiring, it is crucial to determine how homologous interregional connectivities of the left and right hemispheres are genetically determined and related. To address this, in the present study, we analyzed human twin and pedigree samples with high-quality diffusion magnetic resonance imaging tractography and estimated the heritability and genetic correlation of homologous left and right white matter (WM) connections. The results showed that the heritability of WM connectivity was similar and coupled between the 2 hemispheres and that the degree of overlap in genetic factors underlying homologous WM connectivity (i.e., interhemispheric genetic correlation) varied substantially across the human brain: from complete overlap to complete nonoverlap. Particularly, the heritability was significantly stronger and the chance of interhemispheric complete overlap in genetic factors was higher in subcortical WM connections than in cortical WM connections. In addition, the heritability and interhemispheric genetic correlations were stronger for long-range connections than for short-range connections. These findings highlight the determinants of the genetics underlying WM connectivity and its interhemispheric relationships, and provide insight into genetic basis of WM connectivity asymmetries in both healthy and disease states.

Functional brain connectivity changes across the human life span: From fetal development to old age

The dynamic of the temporal correlations between brain areas, called functional connectivity (FC), undergoes complex transformations through the life span. In this review, we aim to provide an overview of these changes in the nonpathological brain from fetal life to advanced age. After a brief description of the main methods, we propose that FC development can be divided into four main phases: first, before birth, a strong change in FC leads to the emergence of functional proto-networks, involving mainly within network short-range connections. Then, during the first years of life, there is a strong widespread organization of networks which starts with segregation processes followed by a continuous increase in integration. Thereafter, from adolescence to early adulthood, a refinement of existing networks in the brain occurs, characterized by an increase in integrative processes until about 40 years. Middle age constitutes a pivotal period associated with an inversion of the functional brain trajectories with a decrease in segregation process in conjunction to a large-scale reorganization of between network connections. Studies suggest that these processes are in line with the development of cognitive and sensory functions throughout life as well as their deterioration. During aging, results support the notion of dedifferentiation processes, which refer to the decrease in functional selectivity of the brain regions, resulting in more diffuse and less specialized FC, associated with the disruption of cognitive functions with age. The inversion of developmental processes during aging is in accordance with the developmental models of neuroanatomy for which the latest matured regions are the first to deteriorate.