Population-based tract-to-region connectome of the human brain and its hierarchical topology

Mapping the structure-function relationship along macroscale gradients in the human brain

Functional coactivation between human brain regions is partly explained by white matter connections; however, how the structure-function relationship varies by function remains unclear. Here, we reference large data repositories to compute maps of structure-function correspondence across hundreds of specific functions and brain regions. We use natural language processing to accurately predict structure-function correspondence for specific functions and to identify macroscale gradients across the brain that correlate with structure-function correspondence as well as cortical thickness. Our findings suggest structure-function correspondence unfolds along a sensory-fugal organizational axis, with higher correspondence in primary sensory and motor cortex for perceptual and motor functions, and lower correspondence in association cortex for cognitive functions. Our study bridges neuroscience and natural language to describe how structure-function coupling varies by region and function in the brain, offering insight into the diversity and evolution of neural network properties.

Menstrual cycle-driven hormone concentrations co-fluctuate with white and gray matter architecture changes across the whole brain

Cyclic fluctuations in hypothalamic-pituitary-gonadal axis (HPG-axis) hormones exert powerful behavioral, structural, and functional effects through actions on the mammalian central nervous system. Yet, very little is known about how these fluctuations alter the structural nodes and information highways of the human brain. In a study of 30 naturally cycling women, we employed multidimensional diffusion and T1-weighted imaging during three estimated menstrual cycle phases (menses, ovulation, and mid-luteal) to investigate whether HPG-axis hormone concentrations co-fluctuate with alterations in white matter (WM) microstructure, cortical thickness (CT), and brain volume. Across the whole brain, 17β-estradiol and luteinizing hormone (LH) concentrations were directly proportional to diffusion anisotropy (μFA; 17β-estradiol: β1 = 0.145, highest density interval (HDI) = [0.211, 0.4]; LH: β1 = 0.111, HDI = [0.157, 0.364]), while follicle-stimulating hormone (FSH) was directly proportional to CT (β1 = 0 .162, HDI = [0.115, 0.678]). Within several individual regions, FSH and progesterone demonstrated opposing relationships with mean diffusivity (Diso) and CT. These regions mainly reside within the temporal and occipital lobes, with functional implications for the limbic and visual systems. Finally, progesterone was associated with increased tissue (β1 = 0.66, HDI = [0.607, 15.845]) and decreased cerebrospinal fluid (CSF; β1 = -0.749, HDI = [-11.604, -0.903]) volumes, with total brain volume remaining unchanged. These results are the first to report simultaneous brain-wide changes in human WM microstructure and CT coinciding with menstrual cycle-driven hormone rhythms. Effects were observed in both classically known HPG-axis receptor-dense regions (medial temporal lobe, prefrontal cortex) and in other regions located across frontal, occipital, temporal, and parietal lobes. Our results suggest that HPG-axis hormone fluctuations may have significant structural impacts across the entire brain.

Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships

The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. However, few tools exist to support the development and evaluation of predictive models in the context of neuroimaging or lesion-behavior research, creating an obstacle to the widespread adoption of predictive modeling approaches in the field. Further, existing tools for lesion-behavior analysis are often unable to accommodate categorical outcome variables and often impose restrictions on the predictor data. Researchers therefore often must use different software packages and analytical approaches depending on whether they are addressing a classification vs. regression problem and on whether their predictor data correspond to binary lesion images, continuous lesion-network images, connectivity matrices, or other data modalities. To address these limitations, we have developed a MATLAB software toolkit that supports both inferential and predictive modeling frameworks, accommodates both classification and regression problems, and does not impose restrictions on the modality of the predictor data. The toolkit features both a graphical user interface and scripting interface, includes implementations of multiple mass-univariate, multivariate, and machine learning models, features built-in and customizable routines for hyper-parameter optimization, cross-validation, model stacking, and significance testing, and automatically generates text-based descriptions of key methodological details and modeling results to improve reproducibility and minimize errors in the reporting of methods and results. Here, we provide an overview and discussion of the toolkit's features and demonstrate its functionality by applying it to the question of how expressive and receptive language impairments relate to lesion location, structural disconnection, and functional network disruption in a large sample of patients with left hemispheric brain lesions. We find that impairments in expressive vs. receptive language are most strongly associated with left lateral prefrontal and left posterior temporal/parietal damage, respectively. We also find that impairments in expressive vs. receptive language are associated with partially overlapping patterns of fronto-temporal structural disconnection, and that the associated functional networks are also similar. Importantly, we find that lesion location and lesion-derived network measures are highly predictive of both types of impairment, with predictions from models trained on these measures explaining ~30-40% of the variance on average when applied to data from patients not used to train the models. We have made the toolkit publicly available, and we have included a comprehensive set of tutorial notebooks to support new users in applying the toolkit in their studies.

Outcomes and Mechanisms Associated With Selective Thalamic Neuronal Loss in Chronic Traumatic Brain Injury

Importance

The chronic neuronal burden of traumatic brain injury (TBI) is not fully characterized by routine imaging, limiting understanding of the role of neuronal substrates in adverse outcomes.

Objective

To determine whether tissues that appear healthy on routine imaging can be investigated for selective neuronal loss using [11C]flumazenil (FMZ) positron emission tomography (PET) and to examine whether this neuronal loss is associated with long-term outcomes.

Design, Setting, and Participants

In this cross-sectional study, data were collected prospectively from 2 centers (University of Cambridge in the UK and Weill Cornell Medicine in the US) between September 1, 2004, and May 31, 2021. Patients with TBI (>6 months postinjury) were compared with healthy control participants (all aged >18 years). Individuals with neurological disease, benzodiazepine use, or contraindication to magnetic resonance imaging were excluded. Data were retrospectively collated with nonconsecutive recruitment, owing to convenience and scanner or PET ligand availability. Data were analyzed between February 1 and September 30, 2023.

Exposure

Flumazenil voxelwise binding potential relative to nondisplaceable binding potential (BPND).

Main Outcomes and Measures

Selective neuronal loss identified with FMZ PET was compared between groups on voxelwise and regional scales, and its association with functional, cognitive, and psychological outcomes was examined using Glasgow Outcome Scale (GOS) scores, measures of sustained executive attention (animal and sustained fluency), and 36-Item Short Form Health Survey (SF-36) scores. Diffusion tensor imaging was used to assess structural connectivity of regions of cortical damage, and its association with thalamic selective neuronal loss.

Results

In this study, 24 patients with chronic TBI (mean [SD] age, 39.2 [12.3] years; 18 men [75.0%]) and 33 healthy control participants (mean [SD] age, 47.6 [20.5] years; 23 men [69.7%]) underwent FMZ PET. Patients with TBI had a median time of 29 (range, 7-95) months from injury to scan. They displayed selective neuronal loss in thalamic nuclei, over and above gross volume loss in the left thalamus, and bilateral central, mediodorsal, ventral-lateral dorsal, anterior, and ventral anterior thalamic nuclei, across a wide range of injury severities. Neuronal loss was associated with worse functional outcome using GOS scores (left thalamus, left ventral anterior, and bilateral central, mediodorsal, and anterior nuclei), worse cognitive outcome on measures of sustained executive attention (left thalamus, bilateral central, and right mediodorsal nuclei), and worse emotional outcome using SF-36 scores (right central thalamic nucleus). Chronic thalamic neuronal loss partially mirrored the location of primary cortical contusions, which may indicate secondary injury mechanisms of transneuronal degeneration.

Conclusions and Relevance

The findings of this study suggest that selective thalamic vulnerability may have chronic neuronal consequences with relevance to long-term outcome, suggesting the evolving and potentially lifelong thalamic neuronal consequences of TBI. FMZ PET is a more sensitive marker of the burden of neuronal injury than routine imaging; therefore, it could inform outcome prognostication and may lead to the development of individualized precision medicine approaches.

Structural Brain Connectivity Guided Optimal Contact Selection for Deep Brain Stimulation of the Subthalamic Nucleus

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective therapy in ameliorating the motor symptoms of Parkinson disease. However, postoperative optimal contact selection is crucial for achieving the best outcome of deep brain stimulation of the subthalamic nucleus surgery, but the process is currently a trial-and-error and time-consuming procedure that relies heavily on surgeons' clinical experience.

METHODS

In this study, we propose a structural brain connectivity guided optimal contact selection method for deep brain stimulation of the subthalamic nucleus. Firstly, we reconstruct the DBS electrode location and estimate the stimulation range using volume of tissue activated from each DBS contact. Then, we extract the structural connectivity features by concatenating fractional anisotropy and the number of streamlines features of activated regions and the whole brain regions. Finally, we use a convolutional neural network with convolutional block attention module to identify the structural connectivity features for the optimal contact selection.

RESULTS

We review the data of 800 contacts from 100 patients with Parkinson disease for the experiment. The proposed method achieves promising results, with the average accuracy of 97.63%, average precision of 94.50%, average recall of 94.46%, and average specificity of 98.18%, respectively. Our method can provide the suggestion for optimal contact selection.

CONCLUSIONS

Our proposed method can improve the efficiency and accuracy of DBS optimal contact selection, reduce the dependence on surgeons' experience, and has the potential to facilitate the development of advanced DBS technology.

Harnessing the frontal aslant tract's structure to assess its involvement in cognitive functions: new insights from 7-T diffusion imaging

The first therapeutical goal followed by neurooncological surgeons dealing with prefrontal gliomas is attempting supramarginal tumor resection preserving relevant neurological function. Therefore, advanced knowledge of the frontal aslant tract (FAT) functional neuroanatomy in high-order cognitive domains beyond language and speech processing would help refine neurosurgeries, predicting possible relevant cognitive adverse events and maximizing the surgical efficacy. To this aim we performed the recently developed correlational tractography analyses to evaluate the possible relationship between FAT's microstructural properties and cognitive functions in 27 healthy subjects having ultra-high-field (7-Tesla) diffusion MRI. We independently assessed FAT segments innervating the dorsolateral prefrontal cortices (dlPFC-FAT) and the supplementary motor area (SMA-FAT). FAT microstructural robustness, measured by the tract's quantitative anisotropy (QA), was associated with a better performance in episodic memory, visuospatial orientation, cognitive processing speed and fluid intelligence but not sustained selective attention tests. Overall, the percentual tract volume showing an association between QA-index and improved cognitive scores (pQACV) was higher in the SMA-FAT compared to the dlPFC-FAT segment. This effect was right-lateralized for verbal episodic memory and fluid intelligence and bilateralized for visuospatial orientation and cognitive processing speed. Our results provide novel evidence for a functional specialization of the FAT beyond the known in language and speech processing, particularly its involvement in several higher-order cognitive domains. In light of these findings, further research should be encouraged to focus on neurocognitive deficits and their impact on patient outcomes after FAT damage, especially in the context of glioma surgery.

Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning

Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and clinical research. However, it is challenging to segment SWM due to the short adjacent gyri connection in a U-shaped pattern. In this work, we propose an Anatomically-guided Superficial Fiber Segmentation (Anat-SFSeg) framework to improve the performance on SWM segmentation. The framework consists of a unique fiber anatomical descriptor (named FiberAnatMap) and a deep learning network based on point-cloud data. The spatial coordinates of fibers represented as point clouds, as well as the anatomical features at both the individual and group levels, are fed into a neural network. The network is trained on Human Connectome Project (HCP) datasets and tested on the subjects with a range of cognitive impairment levels. One new metric named fiber anatomical region proportion (FARP), quantifies the ratio of fibers in the defined brain regions and enables the comparison with other methods. Another metric named anatomical region fiber count (ARFC), represents the average fiber number in each cluster for the assessment of inter-subject differences. The experimental results demonstrate that Anat-SFSeg achieves the highest accuracy on HCP datasets and exhibits great generalization on clinical datasets. Diffusion tensor metrics and ARFC show disorder severity associated alterations in patients with Alzheimer's disease (AD) and mild cognitive impairments (MCI). Correlations with cognitive grades show that these metrics are potential neuroimaging biomarkers for AD. Furthermore, Anat-SFSeg could be utilized to explore other neurodegenerative, neurodevelopmental or psychiatric disorders.

Unveiling the axonal connectivity between the precuneus and temporal pole: Structural evidence from the cingulum pathways

Neuroimaging studies have consistently demonstrated concurrent activation of the human precuneus and temporal pole (TP), both during resting-state conditions and various higher-order cognitive functions. However, the precise underlying structural connectivity between these brain regions remains uncertain despite significant advancements in neuroscience research. In this study, we investigated the connectivity of the precuneus and TP by employing parcellation-based fiber micro-dissections in human brains and fiber tractography techniques in a sample of 1065 human subjects and a sample of 41 rhesus macaques. Our results demonstrate the connectivity between the posterior precuneus area POS2 and the areas 35, 36, and TG of the TP via the fifth subcomponent of the cingulum (CB-V) also known as parahippocampal cingulum. This finding contributes to our understanding of the connections within the posteromedial cortices, facilitating a more comprehensive integration of anatomy and function in both normal and pathological brain processes. PRACTITIONER POINTS: Our investigation delves into the intricate architecture and connectivity patterns of subregions within the precuneus and temporal pole, filling a crucial gap in our knowledge. We revealed a direct axonal connection between the posterior precuneus (POS2) and specific areas (35, 35, and TG) of the temporal pole. The direct connections are part of the CB-V pathway and exhibit a significant association with the cingulum, SRF, forceps major, and ILF. Population-based human tractography and rhesus macaque fiber tractography showed consistent results that support micro-dissection outcomes.

A sensorimotor-association axis of thalamocortical connection development

Human cortical development follows a sensorimotor-to-association sequence during childhood and adolescence1-6. The brain's capacity to enact this sequence over decades indicates that it relies on intrinsic mechanisms to regulate inter-regional differences in the timing of cortical maturation, yet regulators of human developmental chronology are not well understood. Given evidence from animal models that thalamic axons modulate windows of cortical plasticity7-12, here we evaluate the overarching hypothesis that structural connections between the thalamus and cortex help to coordinate cortical maturational heterochronicity during youth. We first introduce, cortically annotate, and anatomically validate a new atlas of human thalamocortical connections using diffusion tractography. By applying this atlas to three independent youth datasets (ages 8-23 years; total N = 2,676), we reproducibly demonstrate that thalamocortical connections develop along a maturational gradient that aligns with the cortex's sensorimotor-association axis. Associative cortical regions with thalamic connections that take longest to mature exhibit protracted expression of neurochemical, structural, and functional markers indicative of higher circuit plasticity as well as heightened environmental sensitivity. This work highlights a central role for the thalamus in the orchestration of hierarchically organized and environmentally sensitive windows of cortical developmental malleability.

Differential tractography and whole brain connectometry in primary motor area gliomas resection: A feasibility study

OBJECTIVE

Establish the evolution of the connectome before and after resection of motor area glioma using a comparison of connectome maps and high-definition differential tractography (DifT).

METHODS

DifT was done using normalized quantitative anisotropy (NQA) with DSI Studio. The quantitative analysis involved obtaining mean NQA and fractional anisotropy (FA) values for the disrupted pathways tracing the corticospinal tract (CST), and white fiber network changes over time.

RESULTS

We described the baseline tractography, DifT, and white matter network changes from two patients who underwent resection of an oligodendroglioma (Case 1) and an IDH mutant astrocytoma, grade 4 (Case 2).

CASE 1

There was a slight decrease in the diffusion signal of the compromised CST in the immediate postop. The NQA and FA values increased at the 1-year follow-up (0.18 vs. 0.32 and 0.35 vs. 0.44, respectively).

CASE 2

There was an important decrease in the immediate postop, followed by an increase in the follow-up. In the 1-year follow-up, the patient presented with radiation necrosis and tumor recurrence, increasing NQA from 0.18 in the preop to 0.29. Fiber network analysis: whole-brain connectome comparison demonstrated no significant changes in the immediate postop. However, in the 1-year follow up there was a notorious reorganization of the fibers in both cases, showing the decreased density of connections.

CONCLUSIONS

Connectome studies and DifT constitute new potential tools to predict early reorganization changes in a patient's networks, showing the brain plasticity capacity, and helping to establish timelines for the progression of the tumor and treatment-induced changes.

Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients

Introduction

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks.

Methods & results

We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance.

Conclusion

Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Four Streams Within the Prefrontal Cortex: Integrating Structural and Functional Connectivity

Merging functional evidence derived from studies of autism spectrum disorder and attention-deficit/hyperactivity disorder converges in four neural streams of the prefrontal cortex, hence suggesting a model of information processing through four streams: motor through Brodmann area (BA) 8, emotion through BA 9, memory through BA 10, and emotional-related sensory through BA 11. A growing body of functional data has been supporting this model of information processing. Nevertheless, the underlying structural connectivity was only recently unveiled by a population-based high-definition tractography study with data from 1,065 individuals. This update provides a brief overview of recent evidence supporting the anatomofunctional integration of the four streams of the prefrontal cortex and reviews the white matter fiber tracts subserving the four streams.

Distinct patterns of voxel- and connection-based white matter hyperintensity distribution and associated factors in early-onset and late-onset Alzheimer's disease

Introduction

The distribution of voxel- and connection-based white matter hyperintensity (WMH) patterns in early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD), as well as factors associated with these patterns, remain unclear.

Method

We analyzed the WMH distribution patterns in EOAD and LOAD at the voxel and connection levels, each compared with their age-matched cognitively unimpaired participants. Linear regression assessed the independent effects of amyloid and vascular risk factors on WMH distribution patterns in both groups.

Results

Patients with EOAD showed increased WMH burden in the posterior region at the voxel level, and in occipital region tracts and visual network at the connection level, compared to controls. LOAD exhibited extensive involvement across various brain areas in both levels. Amyloid accumulation was associated WMH distribution in the early-onset group, whereas the late-onset group demonstrated associations with both amyloid and vascular risk factors.

Discussion

EOAD showed posterior-focused WMH distribution pattern, whereas LOAD was with a wider distribution. Amyloid accumulation was associated with connection-based WMH patterns in both early-onset and late-onset groups, with additional independent effects of vascular risk factors in late-onset group.

Highlights

Both early-onset Alzheimer's disease (EOAD) and late-onset AD (LOAD) showed increased white matter hyperintensity (WMH) volume compared with their age-matched cognitively unimpaired participants.EOAD and LOAD exhibited distinct patterns of WMH distribution, with EOAD showing a posterior-focused pattern and LOAD displaying a wider distribution across both voxel- and connection-based levels.In both EOAD and LOAD, amyloid accumulation was associated with connection-based WMH patterns, with additional independent effects of vascular risk factors observed in LOAD.

Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease

Introduction

Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes.

Materials and methods

Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics.

Results

Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs.

Discussion

We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.

Head injury: Importance of the deep brain nuclei in force transmission to the brain

Finite element modeling provides a digital representation of the human body. It is currently the most pertinent method to study the mechanisms of head injury, and is becoming a scientific reference in forensic expert reports. Improved biofidelity is a recurrent aim of research studies in biomechanics in order to improve earlier models whose mechanical properties conformed to simplified elastic behavior and mechanic laws. We aimed to study force transmission to the brain following impacts to the head, using a finite element head model with increased biofidelity. To the model developed by the Laboratory of Applied Biomechanics of Marseille, we added new brain structures (thalamus, central gray nuclei and ventricular systems) as well as three tracts involved in the symptoms of head injury: the corpus callosum, uncinate tracts and corticospinal tracts. Three head impact scenarios were simulated: an uppercut with the prior model and an uppercut with the improved model in order to compare the two models, and a lateral impact with an impact velocity of 6.5 m/s in the improved model. In these conditions, in uppercuts the maximum stress values did not exceed the injury risk threshold. On the other hand, the deep gray matter (thalamus and central gray nuclei) was the region at highest risk of injury during lateral impacts. Even if injury to the deep gray matter is not immediately life-threatening, it could explain the chronic disabling symptoms of even low-intensity head injury.

Probabilistic coverage of the frontal aslant tract in young adults: Insights into individual variability, lateralization, and language functions

The frontal aslant tract (FAT) is a crucial neural pathway of language and speech, but little is known about its connectivity and segmentation differences across populations. In this study, we investigate the probabilistic coverage of the FAT in a large sample of 1065 young adults. Our primary goal was to reveal individual variability and lateralization of FAT and its structure-function correlations in language processing. The study utilized diffusion MRI data from 1065 subjects obtained from the Human Connectome Project. Automated tractography using DSI Studio software was employed to map white matter bundles, and the results were examined to study the population variation of the FAT. Additionally, anatomical dissections were performed to validate the fiber tracking results. The tract-to-region connectome, based on Human Connectome Project-MMP parcellations, was utilized to provide population probability of the tract-to-region connections. Our results showed that the left anterior FAT exhibited the most substantial individual differences, particularly in the superior and middle frontal gyrus, with greater variability in the superior than the inferior region. Furthermore, we found left lateralization in FAT, with a greater difference in coverage in the inferior and posterior portions. Additionally, our analysis revealed a significant positive correlation between the left FAT inferior coverage area and the performance on the oral reading recognition (p = .016) and picture vocabulary (p = .0026) tests. In comparison, fractional anisotropy of the right FAT exhibited marginal significance in its correlation (p = .056) with Picture Vocabulary Test. Our findings, combined with the connectivity patterns of the FAT, allowed us to segment its structure into anterior and posterior segments. We found significant variability in FAT coverage among individuals, with left lateralization observed in both macroscopic shape measures and microscopic diffusion metrics. Our findings also suggested a potential link between the size of the left FAT's inferior coverage area and language function tests. These results enhance our understanding of the FAT's role in brain connectivity and its potential implications for language and executive functions.

White matter tracts and executive functions: a review of causal and correlation evidence

Executive functions are high-level cognitive processes involving abilities such as working memory/updating, set-shifting and inhibition. These complex cognitive functions are enabled by interactions among widely distributed cognitive networks, supported by white matter tracts. Executive impairment is frequent in neurological conditions affecting white matter; however, whether specific tracts are crucial for normal executive functions is unclear. We review causal and correlation evidence from studies that used direct electrical stimulation during awake surgery for gliomas, voxel-based and tract-based lesion-symptom mapping, and diffusion tensor imaging to explore associations between the integrity of white matter tracts and executive functions in healthy and impaired adults. The corpus callosum was consistently associated with all executive processes, notably its anterior segments. Both causal and correlation evidence showed prominent support of the superior longitudinal fasciculus to executive functions, notably to working memory. More specifically, strong evidence suggested that the second branch of the superior longitudinal fasciculus is crucial for all executive functions, especially for flexibility. Global results showed left lateralization for verbal tasks and right lateralization for executive tasks with visual demands. The frontal aslant tract potentially supports executive functions, however, additional evidence is needed to clarify whether its involvement in executive tasks goes beyond the control of language. Converging evidence indicates that a right-lateralized network of tracts connecting cortical and subcortical grey matter regions supports the performance of tasks assessing response inhibition, some suggesting a role for the right anterior thalamic radiation. Finally, correlation evidence suggests a role for the cingulum bundle in executive functions, especially in tasks assessing inhibition. We discuss these findings in light of current knowledge about the functional role of these tracts, descriptions of the brain networks supporting executive functions and clinical implications for individuals with brain tumours.

Preserved Corticospinal Tract Revealed by Acute Perfusion Imaging Relates to Better Outcome After Thrombectomy in Stroke

BACKGROUND

The indication for mechanical thrombectomy (MT) in stroke patients with large vessel occlusion has been constantly expanded over the past years. Despite remarkable treatment effects at the group level in clinical trials, many patients remain severely disabled even after successful recanalization. A better understanding of this outcome variability will help to improve clinical decision-making on MT in the acute stage. Here, we test whether current outcome models can be refined by integrating information on the preservation of the corticospinal tract as a functionally crucial white matter tract derived from acute perfusion imaging.

METHODS

We retrospectively analyzed 162 patients with stroke and large vessel occlusion of the anterior circulation who were admitted to the University Medical Center Lübeck between 2014 and 2020 and underwent MT. The ischemic core was defined as fully automatized based on the acute computed tomography perfusion with cerebral blood volume data using outlier detection and clustering algorithms. Normative whole-brain structural connectivity data were used to infer whether the corticospinal tract was affected by the ischemic core or preserved. Ordinal logistic regression models were used to correlate this information with the modified Rankin Scale after 90 days.

RESULTS

The preservation of the corticospinal tract was associated with a reduced risk of a worse functional outcome in large vessel occlusion-stroke patients undergoing MT, with an odds ratio of 0.28 (95% CI, 0.15-0.53). This association was still significant after adjusting for multiple confounding covariables, such as age, lesion load, initial symptom severity, sex, stroke side, and recanalization status.

CONCLUSIONS

A preinterventional computed tomography perfusion-based surrogate of corticospinal tract preservation or disconnectivity is strongly associated with functional outcomes after MT. If validated in independent samples this concept could serve as a novel tool to improve current outcome models to better understand intersubject variability after MT in large vessel occlusion stroke.

Network coupling and surgical treatment response in temporal lobe epilepsy: A proof-of-concept study

OBJECTIVE

This proof-of-concept study aimed to examine the overlap between structural and functional activity (coupling) related to surgical response.

METHODS

We studied intracranial rest and ictal stereoelectroencephalography (sEEG) recordings from 77 seizures in thirteen participants with temporal lobe epilepsy (TLE) who subsequently underwent resective/laser ablation surgery. We used the stereotactic coordinates of electrodes to construct functional (sEEG electrodes) and structural connectomes (diffusion tensor imaging). A Jaccard index was used to assess the similarity (coupling) between structural and functional connectivity at rest and at various intraictal timepoints.

RESULTS

We observed that patients who did not become seizure free after surgery had higher connectome coupling recruitment than responders at rest and during early and mid seizure (and visa versa).

SIGNIFICANCE

Structural networks provide a backbone for functional activity in TLE. The association between lack of seizure control after surgery and the strength of synchrony between these networks suggests that surgical intervention aimed to disrupt these networks may be ineffective in those that display strong synchrony. Our results, combined with findings of other groups, suggest a potential mechanism that explains why certain patients benefit from epilepsy surgery and why others do not. This insight has the potential to guide surgical planning (e.g., removal of high coupling nodes) following future research.

ReeBundle: A Method for Topological Modeling of White Matter Pathways Using Diffusion MRI

Tractography can generate millions of complex curvilinear fibers (streamlines) in 3D that exhibit the geometry of white matter pathways in the brain. Common approaches to analyzing white matter connectivity are based on adjacency matrices that quantify connection strength but do not account for any topological information. A critical element in neurological and developmental disorders is the topological deterioration and irregularities in streamlines. In this paper, we propose a novel Reeb graph-based method "ReeBundle" that efficiently encodes the topology and geometry of white matter fibers. Given the trajectories of neuronal fiber pathways (neuroanatomical bundle), we re-bundle the streamlines by modeling their spatial evolution to capture geometrically significant events (akin to a fingerprint). ReeBundle parameters control the granularity of the model and handle the presence of improbable streamlines commonly produced by tractography. Further, we propose a new Reeb graph-based distance metric that quantifies topological differences for automated quality control and bundle comparison. We show the practical usage of our method using two datasets: (1) For International Society for Magnetic Resonance in Medicine (ISMRM) dataset, ReeBundle handles the morphology of the white matter tract configurations due to branching and local ambiguities in complicated bundle tracts like anterior and posterior commissures; (2) For the longitudinal repeated measures in the Cognitive Resilience and Sleep History (CRASH) dataset, repeated scans of a given subject acquired weeks apart lead to provably similar Reeb graphs that differ significantly from other subjects, thus highlighting ReeBundle's potential for clinical fingerprinting of brain regions.

The anatomy of the four streams of the prefrontal cortex. Preliminary evidence from a population based high definition tractography study

The model of the four streams of the prefrontal cortex proposes 4 streams of information: motor through Brodmann area (BA) 8, emotion through BA 9, memory through BA 10, and emotional-related sensory through BA 11. Although there is a surge of functional data supporting these 4 streams within the PFC, the structural connectivity underlying these neural networks has not been fully clarified. Here we perform population-based high-definition tractography using an averaged template generated from data of 1,065 human healthy subjects acquired from the Human Connectome Project to further elucidate the structural organization of these regions. We report the structural connectivity of BA 8 with BA 6, BA 9 with the insula, BA 10 with the hippocampus, BA 11 with the temporal pole, and BA 11 with the amygdala. The 4 streams of the prefrontal cortex are subserved by a structural neural network encompassing fibers of the anterior part of the superior longitudinal fasciculus-I and II, corona radiata, cingulum, frontal aslant tract, and uncinate fasciculus. The identified neural network of the four streams of the PFC will allow the comprehensive analysis of these networks in normal and pathological brain function.

Incorporation of Brain Connectomics for Stereotactic Radiosurgery Treatment Planning

BACKGROUND AND IMPORTANCE

Neurosurgeons have integrated neuroanatomy-based tractography to avoid critical structures during dose planning. However, they have yet to integrate more comprehensive connectome networks for radiosurgical planning.

CLINICAL PRESENTATION

A young man presented with a Spetzler-Martin Grade 3 right temporal arteriovenous malformation.

DISCUSSION

As proof of concept, we incorporated connectomic networks including default mode network, optic radiation and central executive network into the Gamma Knife radiosurgical treatment planning workflow. Connectome networks were created from T1 anatomic and diffusion-weighted images magnetic resonance images using Quicktome software. The resulting networks were voxel-encoded in the magnetic resonance images, imported into GammaPlan, and segmented by image thresholding. The GammaPlan Lightning optimizer was used to create radiosurgical plans with a dose of 20 Gy to the 50% isodose line delivered to the arteriovenous malformation nidus both with and without treating these networks as risk structures. When taking into account the connectome networks, a maximum dose restriction of 14 Gy was placed on each network during lightning dose planning. With default mode network, optic radiation, and central executive network as risk structures, the maximum dose and V 12Gy were reduced by 23.4% and 88.3%, 20% and 34.3%, and 29.8% and 63.2%, respectively.

CONCLUSION

We were able to incorporate connectomes into radiosurgical dose planning approaches. This allowed for dose reductions to the networks while still achieving delivery of a therapeutic dose to the target volume.

The shared white matter developmental trajectory anomalies of attention-deficit/hyperactivity disorder and autism spectrum disorders: A meta-analysis of diffusion tensor imaging studies

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) show common brain area abnormalities, which may contribute to the high shared co-occurrence symptoms and comorbidity of the two disorders. However, neuroanatomic anomalies in neurodevelopmental disorders may change over the course of development, and the developmental variation of these two disorders is unclear. Our study conducted a systematic literature search of PubMed, Web of Science, and EMBASE databases to identify disorder-shared abnormalities of white matter (WM) from childhood to adulthood in ADHD and ASD. 28 ADHD and 23 ASD datasets were included in this meta-analysis and were analysed by AES-SDM to detect differences in fractional anisotropy in patients compared to typically developing individuals. Our main findings reveal the variable WM developmental trajectories in ADHD and ASD respectively, and the two disorders showed overlapping corpus callosum tract abnormalities in their development from children to adults. Furthermore, the overlapping abnormalities of the corpus callosum tract increased with age, which may be related to their gradually increasing shared symptoms and comorbidity in these two disorders.

Thalamostriatal disconnection underpins long-term seizure freedom in frontal lobe epilepsy surgery

Around 50% of patients undergoing frontal lobe surgery for focal drug-resistant epilepsy become seizure free post-operatively; however, only about 30% of patients remain seizure free in the long-term. Early seizure recurrence is likely to be caused by partial resection of the epileptogenic lesion, whilst delayed seizure recurrence can occur even if the epileptogenic lesion has been completely excised. This suggests a coexistent epileptogenic network facilitating ictogenesis in close or distant dormant epileptic foci. As thalamic and striatal dysregulation can support epileptogenesis and disconnection of cortico-thalamostriatal pathways through hemispherotomy or neuromodulation can improve seizure outcome regardless of focality, we hypothesize that projections from the striatum and the thalamus to the cortex may contribute to this common epileptogenic network. To this end, we retrospectively reviewed a series of 47 consecutive individuals who underwent surgery for drug-resistant frontal lobe epilepsy. We performed voxel-based and tractography disconnectome analyses to investigate shared patterns of disconnection associated with long-term seizure freedom. Seizure freedom after 3 and 5 years was independently associated with disconnection of the anterior thalamic radiation and anterior cortico-striatal projections. This was also confirmed in a subgroup of 29 patients with complete resections, suggesting these pathways may play a critical role in supporting the development of novel epileptic networks. Our study indicates that network dysfunction in frontal lobe epilepsy may extend beyond the resection and putative epileptogenic zone. This may be critical in the pathogenesis of delayed seizure recurrence as thalamic and striatal networks may promote epileptogenesis and disconnection may underpin long-term seizure freedom.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.

Developmental trajectory of transmission speed in the human brain

The structure of the human connectome develops from childhood throughout adolescence to middle age, but how these structural changes affect the speed of neuronal signaling is not well described. In 74 subjects, we measured the latency of cortico-cortical evoked responses across association and U-fibers and calculated their corresponding transmission speeds. Decreases in conduction delays until at least 30 years show that the speed of neuronal communication develops well into adulthood.

Inter-Regional Proteomic Profiling of the Human Brain Using an Optimized Protein Extraction Method from Formalin-Fixed Tissue to Identify Signaling Pathways

Proteomics offers vast potential for studying the molecular regulation of the human brain. Formalin fixation is a common method for preserving human tissue; however, it presents challenges for proteomic analysis. In this study, we compared the efficiency of two different protein-extraction buffers on three post-mortem, formalin-fixed human brains. Equal amounts of extracted proteins were subjected to in-gel tryptic digestion and LC-MS/MS. Protein, peptide sequence, and peptide group identifications; protein abundance; and gene ontology pathways were analyzed. Protein extraction was superior using lysis buffer containing tris(hydroxymethyl)aminomethane hydrochloride, sodium dodecyl sulfate, sodium deoxycholate, and Triton X-100 (TrisHCl, SDS, SDC, Triton X-100), which was then used for inter-regional analysis. Pre-frontal, motor, temporal, and occipital cortex tissues were analyzed by label free quantification (LFQ) proteomics, Ingenuity Pathway Analysis and PANTHERdb. Inter-regional analysis revealed differential enrichment of proteins. We found similarly activated cellular signaling pathways in different brain regions, suggesting commonalities in the molecular regulation of neuroanatomically-linked brain functions. Overall, we developed an optimized, robust, and efficient method for protein extraction from formalin-fixed human brain tissue for in-depth LFQ proteomics. We also demonstrate herein that this method is suitable for rapid and routine analysis to uncover molecular signaling pathways in the human brain.

Trait anger representation in microstructural white matter tracts: A diffusion MRI study

BACKGROUND

Understanding the microstructure of the brain that underlies emotions is of pivotal importance for psychology and psychiatry. Herein, we investigated white matter (WM) tracts associated with anger using the diffusion magnetic resonance imaging (DMRI) connectometry approach while exploring potential sex differences.

METHODS

225 healthy participants from the LEMON database were evaluated using the State-Trait Anger Expression Inventory (STAXI). WM images were prepared and analyzed with DMRI. Multiple regression models were fitted to address the correlation of local connectomes with STAXI components with age and handedness as covariates.

RESULTS

There were no statistically significant differences in state anger and trait anger between males and females (p = 0.55 and 0.30, respectively). DMRI connectometry revealed that quantitative anisotropy (QA) values in the bilateral corticospinal tract (CST), splenium of corpus callosum (SCC), middle cerebellar peduncle, left inferior cerebellar peduncle, left cingulum, and left fornix were negatively correlated with trait anger and trait anger temperament (TAT) in males. In contrast, the QA values in the bilateral CST and SCC showed a positive correlation with trait anger and TAT in females, which, however, did not reach statistical significance.

LIMITATIONS

The cross-sectional design and self-reported measures of anger limit the generalizability of our results.

CONCLUSIONS

This is the first DMRI connectometry study to investigate WM circuits involved in anger. We found that the pathways associated with the limbic system and movement-related regions were involved in trait anger and anger expression in men, while no brain pathways showed a significant relationship with anger in women.

Joint impact on attention, alertness and inhibition of lesions at a frontal white matter crossroad

In everyday life, information from different cognitive domains - such as visuospatial attention, alertness, and inhibition - needs to be integrated between different brain regions. Early models suggested that completely segregated brain networks control these three cognitive domains. However, more recent accounts, mainly based on neuroimaging data in healthy participants, indicate that different tasks lead to specific patterns of activation within the same, higher-order and "multiple-demand" network. If so, then a lesion to critical substrates of this common network should determine a concomitant impairment in all three cognitive domains. The aim of the present study was to critically investigate this hypothesis, i.e., to identify focal stroke lesions within the network that can concomitantly impact visuospatial attention, alertness and inhibition. We studied an unselected sample of 60 first-ever right-hemispheric, subacute stroke patients using a data-driven, bottom-up approach. Patients performed 12 standardized neuropsychological and oculomotor tests, four per cognitive domain. Principal component analyses revealed a strong relationship between all three cognitive domains: 10 of 12 tests loaded on a first, Common Component. Analysis of the neuroanatomical lesion correlates using different approaches (i.e., Voxel-Based and Tractwise Lesion-Symptom Mapping, Disconnectome maps) provided convergent evidence on the association between severe impairment of this Common Component and lesions at the intersection of Superior Longitudinal Fasciculus II and III, Frontal Aslant Tract and, to a lesser extent, the Putamen and Inferior Fronto-Occipital Fasciculus. Moreover, patients with a lesion involving this region were significantly more impaired in daily living cognition, which provides an ecological validation of our results. A probabilistic functional atlas of the multiple-demand network was performed to confirm the potential relationship between patients' lesion substrates and observed cognitive impairments as a function of the MD-network connectivity disruption. These findings show, for the first time, that a lesion to a specific white matter crossroad can determine a concurrent breakdown in all three considered cognitive domains. Our results support the multiple-demand network model, proposing that different cognitive operations depend on specific collaborators and their interaction, within the same underlying neural network. Our findings also extend this hypothesis by showing (1) the contribution of SLF and FAT to the multiple-demand network, and (2) a critical neuroanatomical intersection, crossed by a vast amount of long-range white matter tracts, many of which interconnect cortical areas of the multiple-demand network. The vulnerability of this crossroad to stroke has specific cognitive and clinical consequences; this has the potential to influence future rehabilitative approaches.