Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation.

The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.

Brain anatomy and dynamics: A commentary on "Does the brain behave like a (complex) network? I. Dynamics" by Papo and Buldú (2024).

Papo and Buldú [1] ask whether the brain truly acts as a network, or whether it is a convenient coincidence that it can be described with the tools of complex network theory, and the emerging field of network neuroscience. After a broad ranging discussion of networkness they explore some of the ways in which the combination of brain structure and dynamics can indeed better be understood as realising a complex network that subserves brain function. To complement and bolster this perspective, which is informed largely from a physics viewpoint, we direct the reader to additional tools, approaches and insights available from applied mathematics that may further help address some of the many remaining open challenges in this field.

Macroscale coupling between structural and effective connectivity in the mouse brain.

Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be considered: the directionality of the structural connectome and limitations in explaining networks functions through an undirected measure such as FC. Here, we employed an accurate directed SC of the mouse brain acquired through viral tracers and compared it with single-subject effective connectivity (EC) matrices derived from a dynamic causal model (DCM) applied to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their respective couplings by conditioning on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks; only within sensory motor networks did we observe connections that align in terms of both effective and structural strength.

Structural and functional asymmetry of the neonatal cerebral cortex.

Features of brain asymmetry have been implicated in a broad range of cognitive processes; however, their origins are still poorly understood. Here we investigated cortical asymmetries in 442 healthy term-born neonates using structural and functional magnetic resonance images from the Developing Human Connectome Project. Our results demonstrate that the neonatal cortex is markedly asymmetric in both structure and function. Cortical asymmetries observed in the term cohort were contextualized in two ways: by comparing them against cortical asymmetries observed in 103 preterm neonates scanned at term-equivalent age, and by comparing structural asymmetries against those observed in 1,110 healthy young adults from the Human Connectome Project. While associations with preterm birth and biological sex were minimal, significant differences exist between birth and adulthood.

Bundle myelin fraction (BMF) mapping of different white matter connections using microstructure informed tractography.

To date, we have scarce information about the relative myelination level of different fiber bundles in the human brain. Indirect evidence comes from postmortem histology data but histological stainings are unable to follow a specific bundle and determine its intrinsic myelination. In this context, quantitative MRI, and diffusion MRI tractography may offer a viable solution by providing, respectively, voxel-wise myelin sensitive maps and the pathways of the major tracts of the brain. Then, "tractometry" can be used to combine these two pieces of information by averaging tissue features (obtained from any voxel-wise map) along the streamlines recovered with diffusion tractography. Although this method has been widely used in the literature, in cases of voxels containing multiple fiber populations (each with different levels of myelination), tractometry provides biased results because the same value will be attributed to any bundle passing through the voxel. To overcome this bias, we propose a new method - named "myelin streamline decomposition" (MySD) - which extends convex optimization modeling for microstructure informed tractography (COMMIT) allowing the actual value measured by a microstructural map to be deconvolved on each individual streamline, thereby recovering unique bundle-specific myelin fractions (BMFs). We demonstrate the advantage of our method with respect to tractometry in well-studied bundles and compare the cortical projection of the obtained bundle-wise myelin values of both methods. We also prove the stability of our approach across different subjects and different MRI sensitive myelin mapping approaches. This work provides a proof-of-concept of in vivo investigations of entire neuronal pathways that, to date, are not possible.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review.

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project.

Language and theory of mind (ToM) are the cognitive capacities that allow for the successful interpretation and expression of meaning. While functional MRI investigations are able to consistently localize language and ToM to specific cortical regions, diffusion MRI investigations point to an inconsistent and sometimes overlapping set of white matter tracts associated with these two cognitive domains. To further examine the white matter tracts that may underlie these domains, we use a two-tensor tractography method to investigate the white matter microstructure of 809 participants from the Human Connectome Project. 20 association white matter tracts (10 in each hemisphere) are uniquely identified by leveraging a neuroanatomist-curated automated white matter tract atlas. The fractional anisotropy (FA), mean diffusivity (MD), and number of streamlines (NoS) are measured for each white matter tract. Performance on neuropsychological assessments of semantic memory (NIH Toolbox Picture Vocabulary Test, TPVT) and emotion perception (Penn Emotion Recognition Test, PERT) are used to measure critical subcomponents of the language and ToM networks, respectively. Regression models are constructed to examine how structural measurements of left and right white matter tracts influence performance across these two assessments. We find that semantic memory performance is influenced by the number of streamlines of the left superior longitudinal fasciculus III (SLF-III), and emotion perception performance is influenced by the number of streamlines of the right SLF-III. Additionally, we find that performance on both semantic memory & emotion perception is influenced by the FA of the left arcuate fasciculus (AF). The results point to multiple, overlapping white matter tracts that underlie the cognitive domains of language and ToM. Results are discussed in terms of hemispheric dominance and concordance with prior investigations.

Neuroanatomical considerations for optimizing thalamic deep brain stimulation in Tourette syndrome.

OBJECTIVE: Deep brain stimulation (DBS) of the centromedian thalamic nucleus has been reportedly used to treat severe Tourette syndrome, yielding promising outcomes. However, it remains unclear how DBS electrode position and stimulation parameters modulate the specific area and related networks. The authors aimed to evaluate the relationships between the anatomical location of stimulation fields and clinical responses, including therapeutic and side effects. METHODS: The authors collected data from 8 patients with Tourette syndrome who were treated with DBS. The authors selected the active contact following threshold tests of acute side effects and gradually increased the stimulation intensity within the therapeutic window such that acute and chronic side effects could be avoided at each programming session. The patients were carefully interviewed, and stimulation-induced side effects were recorded. Clinical outcomes were evaluated using the Yale Global Tic Severity Scale, the Yale-Brown Obsessive-Compulsive Scale, and the Hamilton Depression Rating Scale. The DBS lead location was evaluated in the normalized brain space by using a 3D atlas. The volume of tissue activated was determined, and the associated normative connective analyses were performed to link the stimulation field with the therapeutic and side effects. RESULTS: The mean follow-up period was 10.9 ± 3.9 months. All clinical scales showed significant improvement. Whereas the volume of tissue activated associated with therapeutic effects covers the centromedian and ventrolateral nuclei and showed an association with motor networks, those associated with paresthesia and dizziness were associated with stimulation of the ventralis caudalis and red nucleus, respectively. Depressed mood was associated with the spread of stimulation current to the mediodorsal nucleus and showed an association with limbic networks. CONCLUSIONS: This study addresses the importance of accurate implantation of DBS electrodes for obtaining standardized clinical outcomes and suggests that meticulous programming with careful monitoring of clinical symptoms may improve outcomes.

Connectivity-based parcellation of normal and anatomically distorted human cerebral cortex.

For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology and resection distort the cerebrum. We sought to overcome this problem by developing a novel connectivity-based parcellation approach that can be applied at the single-subject level. Utilizing normative diffusion data, we first developed a machine-learning (ML) classifier to learn the typical structural connectivity patterns of healthy subjects. Specifically, the Glasser HCP atlas was utilized as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas. Using the resultant feature vector, we determined the parcel identity of each voxel in neurosurgical patients (n = 40) and thereby iteratively adjusted the prior. This approach enabled us to create patient-specific maps independent of brain shape and pathological distortion. The supervised ML classifier re-parcellated an average of 2.65% of cortical voxels across a healthy dataset (n = 178) and an average of 5.5% in neurosurgical patients. Our patient dataset consisted of subjects with supratentorial infiltrating gliomas operated on by the senior author who then assessed the validity and practical utility of the re-parcellated diffusion data. We demonstrate a rapid and effective ML parcellation approach to parcellation of the human cortex during anatomical distortion. Our approach overcomes limitations of indiscriminately applying atlas-based registration from healthy subjects by employing a voxel-wise connectivity approach based on individual data.

Non-invasive mapping of cortical categorization function by repetitive navigated transcranial magnetic stimulation.

Over the past years navigated repetitive transcranial magnetic stimulation (nrTMS) had become increasingly important for the preoperative examination and mapping of eloquent brain areas. Among other applications it was demonstrated that the detection of neuropsychological function, such as arithmetic processing or face recognition, is feasible with nrTMS. In order to investigate the mapping of further brain functions, this study aims to investigate the cortical mapping of categorization function via nrTMS. 20 healthy volunteers purely right-handed, with German as mother tongue underwent nrTMS mapping using 5 Hz/10 pulses. 52 cortical spots spread over each hemisphere were stimulated. The task consisted of 80 pictures of living and non-living images, which the volunteers were instructed to categorize while the simulation pulses were applied. The highest error rates for all errors of all subjects were observed in the left hemisphere's posterior middle frontal gyrus (pMFG) with an error rate of 60%, as well as in the right pMFG and posterior supra marginal gyrus (pSMG) (45%). In total the task processing of non-living objects elicited more errors in total, than the recognition of living objects. nrTMS is able to detect cortical categorization function. Moreover, the observed bihemispheric representation, as well as the higher error incidence for the recognition of non-living objects is well in accordance with current literature. Clinical applicability for preoperative mapping in brain tumor patients but also in general neuroscience has to be evaluated as the next step.

Perineuronal Nets in the Prefrontal Cortex of a Schizophrenia Mouse Model: Assessment of Neuroanatomical, Electrophysiological, and Behavioral Contributions.

Schizophrenia is a neurodevelopmental disorder whose etiopathogenesis includes changes in cellular as well as extracellular structures. Perineuronal nets (PNNs) associated with parvalbumin-positive interneurons (PVs) in the prefrontal cortex (PFC) are dysregulated in schizophrenia. However, the postnatal development of these structures along with their associated neurons in the PFC is unexplored, as is their effects on behavior and neural activity. Therefore, in this study, we employed a DISC1 (Disruption in Schizophrenia) mutation mouse model of schizophrenia to assess these developmental changes and tested whether enzymatic digestion of PNNs in the PFC affected schizophrenia-like behaviors and neural activity. Developmentally, we found that the normal formation of PNNs, PVs, and colocalization of these two in the PFC, peaked around PND 22 (postnatal day 22). However, in DISC1, mutation animals from PND 0 to PND 60, both PNNs and PVs were significantly reduced. After enzymatic digestion of PNNs with chondroitinase in adult animals, the behavioral pattern of control animals mimicked that of DISC1 mutation animals, exhibiting reduced sociability, novelty and increased ultrasonic vocalizations, while there was very little change in other behaviors, such as working memory (Y-maze task involving medial temporal lobe) or depression-like behavior (tail-suspension test involving processing via the hypothalamic pituitary adrenal (HPA) axis). Moreover, following chondroitinase treatment, electrophysiological recordings from the PFC exhibited a reduced proportion of spontaneous, high-frequency firing neurons, and an increased proportion of irregularly firing neurons, with increased spike count and reduced inter-spike intervals in control animals. These results support the proposition that the aberrant development of PNNs and PVs affects normal neural operations in the PFC and contributes to the emergence of some of the behavioral phenotypes observed in the DISC1 mutation model of schizophrenia.

MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning.

Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis.

A simple permutation-based test of intermodal correspondence.

Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state-of-the-art methods involve comparing observed group-level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group-level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this article we propose using subject-level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p-value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n-back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference.

Better living through understanding the insula: Why subregions can make all the difference.

Insula function is considered critical for many motivated behaviors, with proposed functions ranging from attention, behavioral control, emotional regulation, goal-directed and aversion-resistant responding. Further, the insula is implicated in many neuropsychiatric conditions including substance abuse. More recently, multiple insula subregions have been distinguished based on anatomy, connectivity, and functional contributions. Generally, posterior insula is thought to encode more somatosensory inputs, which integrate with limbic/emotional information in middle insula, that in turn integrate with cognitive processes in anterior insula. Together, these regions provide rapid interoceptive information about the current or predicted situation, facilitating autonomic recruitment and quick, flexible action. Here, we seek to create a robust foundation from which to understand potential subregion differences, and provide direction for future studies. We address subregion differences across humans and rodents, so that the latter's mechanistic interventions can best mesh with clinical relevance of human conditions. We first consider the insula's suggested roles in humans, then compare subregional studies, and finally describe rodent work. One primary goal is to encourage precision in describing insula subregions, since imprecision (e.g. including both posterior and anterior studies when describing insula work) does a disservice to a larger understanding of insula contributions. Additionally, we note that specific task details can greatly impact recruitment of various subregions, requiring care and nuance in design and interpretation of studies. Nonetheless, the central ethological importance of the insula makes continued research to uncover mechanistic, mood, and behavioral contributions of paramount importance and interest. This article is part of the special Issue on 'Neurocircuitry Modulating Drug and Alcohol Abuse'.

Organization of parietoprefrontal and temporoprefrontal networks in the macaque.

The connectivity among architectonically defined areas of the frontal, parietal, and temporal cortex of the macaque has been extensively mapped through tract-tracing methods. To investigate the statistical organization underlying this connectivity, and identify its underlying architecture, we performed a hierarchical cluster analysis on 69 cortical areas based on their anatomically defined inputs. We identified 10 frontal, four parietal, and five temporal hierarchically related sets of areas (clusters), defined by unique sets of inputs and typically composed of anatomically contiguous areas. Across the cortex, clusters that share functional properties were linked by dominant information processing circuits in a topographically organized manner that reflects the organization of the main fiber bundles in the cortex. This led to a dorsal-ventral subdivision of the frontal cortex, where dorsal and ventral clusters showed privileged connectivity with parietal and temporal areas, respectively. Ventrally, temporofrontal circuits encode information to discriminate objects in the environment, their value, emotional properties, and functions such as memory and spatial navigation. Dorsal parietofrontal circuits encode information for selecting, generating, and monitoring appropriate actions based on visual-spatial and somatosensory information. This organization may reflect evolutionary antecedents, in which the vertebrate pallium, which is the ancestral cortex, was defined by a ventral and lateral olfactory region and a medial hippocampal region.NEW & NOTEWORTHY The study of cortical connectivity is crucial for understanding brain function and disease. We show that temporofrontal and parietofrontal networks in the macaque can be described in terms of circuits among clusters of areas that share similar inputs and functional properties. The resulting overall architecture described a dual subdivision of the frontal cortex, consistent with the main cortical fiber bundles and an evolutionary trend that underlies the organization of the cortex in the macaque.

Heritability and interindividual variability of regional structure-function coupling.

White matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.

A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.

A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general operating principles. We present a strategy that successively maps a relatively detailed biophysical population model, comprising conductance-based Hodgkin-Huxley type neuron models with connectivity rules derived from anatomical data, to various representations with fewer parameters, finishing with a firing rate network model that permits analysis. We apply this methodology to primary visual cortex of higher mammals, focusing on the functional property of stimulus orientation selectivity of receptive fields of individual neurons. The mapping produces compact expressions for the parameters of the abstract model that clearly identify the impact of specific electrophysiological and anatomical parameters on the analytical results, in particular as manifested by specific functional signatures of visual cortex, including input-output sharpening, conductance invariance, virtual rotation and the tilt after effect. Importantly, qualitative differences between model behaviours point out consequences of various simplifications. The strategy may be applied to other neuronal systems with appropriate modifications.

Meta-analytic connectivity modelling of deception-related brain regions.

Brain-based deception research began only two decades ago and has since included a wide variety of contexts and response modalities for deception paradigms. Investigations of this sort serve to better our neuroscientific and legal knowledge of the ways in which individuals deceive others. To this end, we conducted activation likelihood estimation (ALE) and meta-analytic connectivity modelling (MACM) using BrainMap software to examine 45 task-based fMRI brain activation studies on deception. An activation likelihood estimation comparing activations during deceptive versus honest behavior revealed 7 significant peak activation clusters (bilateral insula, left superior frontal gyrus, bilateral supramarginal gyrus, and bilateral medial frontal gyrus). Meta-analytic connectivity modelling revealed an interconnected network amongst the 7 regions comprising both unidirectional and bidirectional connections. Together with subsequent behavioral and paradigm decoding, these findings implicate the supramarginal gyrus as a key component for the sociocognitive process of deception.

Structural brain dynamics across reading development: A longitudinal MRI study from kindergarten to grade 5.

Primary education is the incubator for learning academic skills that help children to become a literate, communicative, and independent person. Over this learning period, nonlinear and regional changes in the brain occur, but how these changes relate to academic performance, such as reading ability, is still unclear. In the current study, we analyzed longitudinal T1 MRI data of 41 children in order to investigate typical cortical development during the early reading stage (end of kindergarten-end of grade 2) and advanced reading stage (end of grade 2-middle of grade 5), and to detect putative deviant trajectories in children with dyslexia. The structural brain change was quantified with a reliable measure that directly calculates the local morphological differences between brain images of two time points, while considering the global head growth. When applying this measure to investigate typical cortical development, we observed that left temporal and temporoparietal regions belonging to the reading network exhibited an increase during the early reading stage and stabilized during the advanced reading stage. This suggests that the natural plasticity window for reading is within the first years of primary school, hence earlier than the typical period for reading intervention. Concerning neurotrajectories in children with dyslexia compared to typical readers, we observed no differences in gray matter development of the left reading network, but we found different neurotrajectories in right IFG opercularis (during the early reading stage) and in right isthmus cingulate (during the advanced reading stage), which could reflect compensatory neural mechanisms.