XTRACT - Standardised protocols for automated tractography in the human and macaque brain

Generalising XTRACT tractography protocols across common macaque brain templates

Non-human primates are extensively used in neuroscience research as models of the human brain, with the rhesus macaque being a prominent example. We have previously introduced a set of tractography protocols (XTRACT) for reconstructing 42 corresponding white matter (WM) bundles in the human and the macaque brain and have shown cross-species comparisons using such bundles as WM landmarks. Our original XTRACT protocols were developed using the F99 macaque brain template. However, additional macaque template brains are becoming increasingly common. Here, we generalise the XTRACT tractography protocol definitions across five macaque brain templates, including the F99, D99, INIA, Yerkes and NMT. We demonstrate equivalence of such protocols in two ways: (a) Firstly by comparing the bodies of the tracts derived using protocols defined across the different templates considered, (b) Secondly by comparing the projection patterns of the reconstructed tracts across the different templates in two cross-species (human-macaque) comparison tasks. The results confirm similarity of all predictions regardless of the macaque brain template used, providing direct evidence for the generalisability of these tractography protocols across the five considered templates.

A map of white matter tracts in a lesser ape, the lar gibbon

The recent development of methods for constructing directly comparable white matter atlases in primate brains from diffusion MRI allows us to probe specializations unique to humans, great apes, and other primate taxa. Here, we constructed the first white matter atlas of a lesser ape using an ex vivo diffusion-weighted scan of a brain from a young adult (5.5 years) male lar gibbon. We find that white matter architecture of the gibbon temporal lobe suggests specializations that are reminiscent of those previously reported for great apes, specifically, the expansion of the arcuate fasciculus and the inferior longitudinal fasciculus in the temporal lobe. Our findings suggest these white matter expansions into the temporal lobe were present in the last common ancestor to hominoids approximately 16 million years ago and were further modified in the great ape and human lineages. White matter atlases provide a useful resource for identifying neuroanatomical differences and similarities between humans and other primate species and provide insight into the evolutionary variation and stasis of brain organization.

Tracing volitional recovery in post-stroke akinetic mutism using longitudinal microstructure imaging: Insights from a single case study

Lesions in the frontal-subcortical circuitry can lead to akinetic mutism (AM) characterized by diminished volition. However, the microstructural changes in the damaged network underlying its recovery remain unknown. Clinical examination and neuropsychological assessment were performed on a patient with post-stroke AM. Multimodal MRI scans were performed at baseline and follow-ups. We used diffusion MRI and biophysical models, specifically utilizing neurite orientation dispersion and density imaging for assessing gray matter microstructure, and fixel-based analysis for the evaluation of white matter. Longitudinal comparisons were performed between the patient and healthy controls. Pronounced recovery of volition was observed after dopamine agonist therapy combined with physical therapy. In addition to infarcts in the bilateral medial cortex, microstructure imaging detected reduced neurite density in extensive areas, specifically in temporal areas and subcortical nuclei, and decreased fiber density of white matter tracts (TFCE-corrected p < .05). Microstructural degeneration in the anterior cingulate cortex and cingulum was relatively persistent (Bonferroni-corrected p < .05). However, most tracts within the frontal-subcortical circuitry showed increased fiber density during the recovery stage. Microstructure of an extensive network may contribute to the disruption and recovery of volition. Fiber density within the frontal-subcortical circuitry could be a promising biomarker indicating volitional recovery.

Lateral thinking: Neurodegeneration of the cortical cholinergic system in Alzheimer's disease

INTRODUCTION

Atrophy of the nucleus basalis of Meynert (NBM) is an early indicator of Alzheimer's disease (AD). However, reduced integrity of the NBM white matter tracts may be more relevant for cognitive impairment and progression to dementia than NBM volume. Research is needed to compare differences in NBM volume and integrity of the lateral and medial NBM tracts across early and later stages of AD progression.

METHODS

187 participants were included in this study who were either healthy controls (HC; n = 50) or had early mild cognitive impairment (EMCI; n = 50), late MCI (LMCI; n = 37), or AD (n = 50). NBM volume was calculated using voxel-based morphometry and mean diffusivity (MD) of the lateral and medial NBM tracts were extracted using probabilistic tractography. Between group differences in NBM volume and tract MD were compared using linear mixed models controlling for age, sex, and either total intracranial volume or MD of a control mask, respectively. Associations between NBM volume and tract MD with executive function, memory, language, and visuospatial function were also analysed.

RESULTS

NBM volume was smallest in AD followed by LMCI (p < 0.0001), with no difference between EMCI and HC. AD had highest MD for both tracts compared to all other groups (p < 0.01). Both MCI groups had higher lateral tract MD compared to HC (p < 0.05). Medial tract MD was higher in LMCI (p = 0.008), but not EMCI (p = 0.09) compared to HC. Higher lateral tract MD was associated with executive function (p = 0.001) and language (p = 0.02).

DISCUSSION

Integrity of the lateral NBM tract is most sensitive to the earliest stages of AD and should be considered an important therapeutic target for early detection and intervention.

Brain white matter microstructural alterations in Behcet's syndrome correlate with cognitive impairment and disease severity: A diffusion tensor imaging study

OBJECTIVES

To evaluate the microstructural integrity of brain white matter tracts in patients with Neuro-Behcet's syndrome (NBS) and Behcet's syndrome (BS) without neurological manifestations using diffusion tensor imaging (DTI) and to investigate potential utility of DTI as a surrogate biomarker of neurocognitive functioning and disease severity.

METHODS

This cross-sectional study comprised 34 NBS patients and 32 BS patients without neurological involvement, identified based on the International Study Group of the Behcet's disease (ISGBD) and the International Consensus Recommendation (ICR) criteria, as well as 33 healthy controls. Cognitive functions, including attention, memory, language, abstraction, executive control, visuospatial skills, and sensorimotor performance were assessed using standardized questionnaires. DTI data were analyzed using tract-based spatial statistics (TBSS) and automated probabilistic tractography to investigate inter-group differences. Subsequently, correlations between tensor-derived parameters of white matter tracts, neurocognitive test scores, and disease severity measures were examined.

RESULTS

DTI revealed decreased fractional anisotropy and increased radial diffusivity, mean diffusivity, and axial diffusivity in both supratentorial and infratentorial white matter in NBS patients, indicating widespread loss of microstructural integrity. Moreover, this loss of integrity was also observed in BS patients without neurological manifestations, albeit to a lesser extent. In NBS patients, certain white matter tracts, including cingulum bundle, were associated with poor cognitive performance across multiple domains and disease severity.

DISCUSSION

DTI findings might potentially serve as a neuroimaging marker to predict the extent of neurocognitive impairment and disease severity associated with central nervous system involvement in BS.

The Influence of Structural Brain Changes on Cognition in the Context of Healthy Aging: Exploring Mediation Effects Through gBAT-The Graphical Brain Association Tool

The contribution of age-related structural brain changes to the well-established link between aging and cognitive decline is not fully defined. While both age-related regional brain atrophy and cognitive decline have been extensively studied, the specific mediating role of age-related regional brain atrophy on cognitive functions is unclear. This study introduces an open-source software tool with a graphical user interface that streamlines advanced whole-brain mediation analyses, enabling researchers to systematically explore how the brain acts as a mediator in relationships between various behavioral and health outcomes. The tool is showcased by investigating regional brain volume as a mediator to determine the contribution of age-related brain volume loss toward cognition in healthy aging. We analyzed regional brain volumes and cognitive testing data (Montreal Cognitive Assessment [MoCA]) from a cohort of 131 neurologically healthy adult participants (mean age 50 ± 20.8 years, range 20-79, 73% females) drawn from the Aging Brain Cohort Study at the University of South Carolina. Using our open-source tool developed for evaluating brain-behavior associations across the brain and optimized for exploring mediation effects, we conducted a series of mediation analyses using participant age as the predictor variable, total MoCA and MoCA subtest scores as the outcome variables, and regional brain volume as potential mediators. Age-related atrophy within specific anatomical networks was found to mediate the relationship between age and cognition across multiple cognitive domains. Specifically, atrophy in bilateral frontal, parietal, and occipital areas, along with widespread subcortical regions mediated the effect of age on total MoCA scores. Various MoCA subscores were influenced by age through atrophy in distinct brain regions. These involved prefrontal regions, sensorimotor cortex, and parieto-occipital areas for executive function subscores, prefrontal and temporo-occipital regions, along with the caudate nucleus for attention and concentration subscores, frontal and parieto-occipital areas, alongside connecting subcortical areas such as the optic tract for visuospatial subscores and frontoparietal areas for language subscores. Brain-based mediation analysis offers a causal framework for evaluating the mediating role of brain structure on the relationship between age and cognition and provides a more nuanced understanding of cognitive aging than previously possible. By validating the applicability and effectiveness of this approach and making it openly available to the scientific community, we facilitate the exploration of causal mechanisms between variables mediated by the brain.

Human Claustrum Connections: Robust In Vivo Detection by DWI-Based Tractography in Two Large Samples

Despite substantial neuroscience research in the last decade revealing the claustrum's prominent role in mammalian forebrain organization, as evidenced by its extraordinarily widespread connectivity pattern, claustrum studies in humans are rare. This is particularly true for studies focusing on claustrum connections. Two primary reasons may account for this situation: First, the intricate anatomy of the human claustrum located between the external and extreme capsule hinders straightforward and reliable structural delineation. In addition, the few studies that used diffusion-weighted-imaging (DWI)-based tractography could not clarify whether in vivo tractography consistently and reliably identifies claustrum connections in humans across different subjects, cohorts, imaging methods, and connectivity metrics. To address these issues, we combined a recently developed deep-learning-based claustrum segmentation tool with DWI-based tractography in two large adult cohorts: 81 healthy young adults from the human connectome project and 81 further healthy young participants from the Bavarian longitudinal study. Tracts between the claustrum and 13 cortical and 9 subcortical regions were reconstructed in each subject using probabilistic tractography. Probabilistic group average maps and different connectivity metrics were generated to assess the claustrum's connectivity profile as well as consistency and replicability of tractography. We found, across individuals, cohorts, DWI-protocols, and measures, consistent and replicable cortical and subcortical ipsi- and contralateral claustrum connections. This result demonstrates robust in vivo tractography of claustrum connections in humans, providing a base for further examinations of claustrum connectivity in health and disease.

Usefulness of automated tractography for outcome prediction in patients with recurrent stroke

[Purpose] To examine the usefulness of automated tractography for predicting outcomes in patients with recurrent stroke. [Participants and Methods] Diffusion tensor imaging was performed in the second week after stroke, and fractional anisotropy was calculated using automated tractography. Three patients with recurrent strokes were included in this study. [Results] Initial computed tomography findings of a 62-year-old man with stuttering speech revealed a hemorrhage in the left thalamus. Fractional anisotropy indicated slight neural damage in the association fibers of both hemispheres. The patient returned to work with mild attention deficit and aphasia. Initial diffusion-weighted imaging of a 75-year-old man with right upper extremity paresis showed high-intensity areas in the left corona radiata. Fractional anisotropy indicated bilateral neural damage to the corticospinal tract. The patient was discharged with severe right upper extremity impairment and a modified gait. Initial diffusion-weighted imaging of a 60-year-old woman with moyamoya disease who experienced a sudden loss of consciousness showed high-intensity areas in the left anterior circulation territories. Fractional anisotropy indicated severe damage to the right hemisphere, the corticospinal tract, and the superior longitudinal fasciculus of the left hemisphere. She was transferred to a nursing home and remained bedridden. [Conclusion] The symptoms identified in this study agreed with automated tractography findings, which suggests that this methodology is useful for predicting recurrent stroke outcomes.

Prefrontal-Limbic Circuitry Is Associated With Reward Sensitivity in Nonhuman Primates

BACKGROUND

Abnormal reward sensitivity is a risk factor for psychiatric disorders, including eating disorders such as overeating and binge-eating disorder, but the brain structural mechanisms that underlie it are not completely understood. Here, we sought to investigate the relationship between multimodal whole-brain structural features and reward sensitivity in nonhuman primates.

METHODS

Reward sensitivity was evaluated through behavioral economic analysis in which monkeys (adult rhesus macaques; 7 female, 5 male) responded for sweetened condensed milk (10%, 30%, 56%), Gatorade, or water using an operant procedure in which the response requirement increased incrementally across sessions (i.e., fixed ratio 1, 3, 10). Animals were divided into high (n = 6) or low (n = 6) reward sensitivity groups based on essential value for 30% milk. Multimodal magnetic resonance imaging was used to measure gray matter volume and white matter microstructure. Brain structural features were compared between groups, and their correlations with reward sensitivity for various stimuli was investigated.

RESULTS

Animals in the high sensitivity group had greater dorsolateral prefrontal cortex, centromedial amygdaloid complex, and middle cingulate cortex volumes than animals in the low sensitivity group. Furthermore, compared with monkeys in the low sensitivity group, high sensitivity monkeys had lower fractional anisotropy in the left dorsal cingulate bundle connecting the centromedial amygdaloid complex and middle cingulate cortex to the dorsolateral prefrontal cortex, and in the left superior longitudinal fasciculus 1 connecting the middle cingulate cortex to the dorsolateral prefrontal cortex.

CONCLUSIONS

These results suggest that neuroanatomical variation in prefrontal-limbic circuitry is associated with reward sensitivity. These brain structural features may serve as predictive biomarkers for vulnerability to food-based and other reward-related disorders.

White matter structural bases for phase accuracy during tapping synchronization

We determined the intersubject association between the rhythmic entrainment abilities of human subjects during a synchronization-continuation tapping task (SCT) and the macro- and microstructural properties of their superficial (SWM) and deep (DWM) white matter. Diffusion-weighted images were obtained from 32 subjects who performed the SCT with auditory or visual metronomes and five tempos ranging from 550 to 950 ms. We developed a method to determine the density of short-range fibers that run underneath the cortical mantle, interconnecting nearby cortical regions (U-fibers). Notably, individual differences in the density of U-fibers in the right audiomotor system were correlated with the degree of phase accuracy between the stimuli and taps across subjects. These correlations were specific to the synchronization epoch with auditory metronomes and tempos around 1.5 Hz. In addition, a significant association was found between phase accuracy and the density and bundle diameter of the corpus callosum (CC), forming an interval-selective map where short and long intervals were behaviorally correlated with the anterior and posterior portions of the CC. These findings suggest that the structural properties of the SWM and DWM in the audiomotor system support the tapping synchronization abilities of subjects, as cortical U-fiber density is linked to the preferred tapping tempo and the bundle properties of the CC define an interval-selective topography.

Structural and functional connectivity associations with anterior cingulate sulcal variability

Sulcation of the anterior cingulate may be defined by presence of a paracingulate sulcus, a tertiary sulcus developing during the third gestational trimester with implications on cognitive function and disease. In this cross-sectional study we examine task-free resting state functional connectivity and diffusion-weighted tract segmentation data from a cohort of healthy adults (< 60-year-old, n = 129), exploring the impact of ipsilateral paracingulate sulcal presence on structural and functional connectivity. Presence of a left paracingulate sulcus was associated with reduced fractional anisotropy in the left cingulum bundle and the left peri-genual and dorsal bundle segments, suggesting reduced structural organisational coherence in these tracts. This association was not observed in the offsite temporal cingulum bundle segment. Left paracingulate sulcal presence was associated with increased left peri-genual radial diffusivity and tract volume possibly suggesting increased U-fibre density in this region. Greater network dispersity was identified in individuals with an absent left paracingulate sulcus by presence of a significant, predominantly intraregional, frontal component of resting state functional connectivity which was not present in individuals with a present left paracingulate sulcus. Seed-based functional connectivity in pre-defined networks was not associated with paracingulate sulcal presence. These results identify a novel association between sulcation and structural connectivity in a healthy adult population with implications for conditions where this variation is of interest. Presence of a left paracingulate sulcus appears to alter local structural and functional connectivity, possibly as a result of the presence of a local network reliant on short association fibres.

Integrating brain function and structure in the study of the human attentional networks: a functionnectome study

Attention is a heterogeneous function theoretically divided into different systems. While functional magnetic resonance imaging (fMRI) has extensively characterized their functioning, the role of white matter in cognitive function has gained recent interest due to diffusion-weighted imaging advancements. However, most evidence relies on correlations between white matter properties and behavioral or cognitive measures. This study used a new method that combines the signal from distant voxels of fMRI images using the probability of structural connection given by high-resolution normative tractography. We analyzed three fMRI datasets with a visual perceptual task and three attentional manipulations: phasic alerting, spatial orienting, and executive attention. The phasic alerting network engaged temporal areas and their communication with frontal and parietal regions, with left hemisphere dominance. The orienting network involved bilateral fronto-parietal and midline regions communicating by association tracts and interhemispheric fibers. The executive attention network engaged a broad set of brain regions and white matter tracts connecting them, with a particular involvement of frontal areas and their connections with the rest of the brain. These results partially confirm and extend previous knowledge on the neural substrates of the attentional system, offering a more comprehensive understanding through the integration of structure and function.

A practical guide for combining functional regions of interest and white matter bundles

Diffusion-weighted imaging (DWI) is the primary method to investigate macro- and microstructure of neural white matter in vivo. DWI can be used to identify and characterize individual-specific white matter bundles, enabling precise analyses on hypothesis-driven connections in the brain and bridging the relationships between brain structure, function, and behavior. However, cortical endpoints of bundles may span larger areas than what a researcher is interested in, challenging presumptions that bundles are specifically tied to certain brain functions. Functional MRI (fMRI) can be integrated to further refine bundles such that they are restricted to functionally-defined cortical regions. Analyzing properties of these Functional Sub-Bundles (FSuB) increases precision and interpretability of results when studying neural connections supporting specific tasks. Several parameters of DWI and fMRI analyses, ranging from data acquisition to processing, can impact the efficacy of integrating functional and diffusion MRI. Here, we discuss the applications of the FSuB approach, suggest best practices for acquiring and processing neuroimaging data towards this end, and introduce the FSuB-Extractor, a flexible open-source software for creating FSuBs. We demonstrate our processing code and the FSuB-Extractor on an openly-available dataset, the Natural Scenes Dataset.

Microstructural Properties of the Cerebellar Peduncles in Children With Developmental Language Disorder

Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of corticostriatal systems, little is known about the role of the cerebellum in DLD. Corticocerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of children with DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not corticocerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech and language acquisition and development.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

C, Howard. TM, Wilkins. KB, Seo. G, Brontë-Stewart. HM. Lateral Thinking: Pathway Specific Neurodegeneration of the Cortical Cholinergic System in Alzheimer's Disease

INTRODUCTION

Atrophy of the nucleus basalis of Meynert (NBM) is an early indicator of Alzheimer's disease (AD). However, reduced integrity of the NBM white matter tracts may be more relevant for cognitive impairment and progression to dementia than NBM volume. Research is needed to compare differences in NBM volume and integrity of the lateral and medial NBM tracts across early and later stages of AD progression.

METHODS

187 participants were included in this study who were either healthy controls (HC; n=50) or had early mild cognitive impairment (EMCI; n=50), late MCI (LMCI; n=37), or AD (n=50). NBM volume was calculated using voxel-based morphometry and mean diffusivity (MD) of the lateral and medial NBM tracts were extracted using probabilistic tractography. Between group differences in NBM volume and tract MD were compared using linear mixed models controlling for age, sex, and either total intracranial volume or MD of a control mask, respectively. Associations between NBM volume and tract MD with executive function, memory, language, and visuospatial function were also analysed.

RESULTS

NBM volume was smallest in AD followed by LMCI (p<0.0001), with no difference between EMCI and HC. AD had highest MD for both tracts compared to all other groups (p<0.001). Both MCI groups had higher lateral tract MD compared to HC (p<0.05). Medial tract MD was higher in LMCI (p=0.008), but not EMCI (p=0.09) compared to HC. Higher lateral tract MD was associated with executive function (p=0.001) and language (p=0.02).

DISCUSSION

Integrity of the lateral NBM tract is most sensitive to the earliest stages of AD and should be considered an important therapeutic target for early detection and intervention.

White matter microstructural integrity as a key to effective propagation of gamma entrainment in humans

Gamma entrainment through sensory stimulation has the potential to reduce the pathology of Alzheimer's disease in mouse models. However, clinical trials in Alzheimer's disease (AD) patients have yielded inconsistent results, necessitating further investigation. This single-center pre-post intervention study aims to explore the influence of white matter microstructural integrity on gamma rhythm propagation from the visual cortex to AD-affected regions in 31 cognitively normal volunteers aged ≥ 65. Gamma rhythm propagation induced by optimal FLS was measured. Diffusion tensor imaging was employed to assess the integrity of white matter tracts of interest. After excluding 5 participants with a deficit in steady-state visually evoked potentials, 26 participants were included in the final analysis. In the linear regression analyses, gamma entrainment was identified as a significant predictor of gamma propagation (p < 0.001). Furthermore, the study identified white matter microstructural integrity as a significant predictor of gamma propagation by flickering light stimulation (p < 0.05), which was specific to tracts that connect occipital and temporal or frontal regions. These findings indicate that, despite robust entrainment of gamma rhythms in the visual cortex, their propagation to other regions may be impaired if the microstructural integrity of the white matter tracts connecting the visual cortex to other areas is compromised. Consequently, our findings have expanded our understanding of the prerequisites for effective gamma entrainment and suggest that future clinical trials utilizing visual stimulation for gamma entrainment should consider white matter tract microstructural integrity for candidate selection and outcome analysis.

Tractometry of Human Visual White Matter Pathways in Health and Disease

Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.

Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Correlates

Chimpanzees (Pan troglodytes) are humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.

Deep brain stimulation induces white matter remodeling and functional changes to brain-wide networks

Deep brain stimulation (DBS) is an emerging therapeutic option for treatment resistant neurological and psychiatric disorders, most notably depression. Despite this, little is known about the anatomical and functional mechanisms that underlie this therapy. Here we targeted stimulation to the white matter adjacent to the subcallosal anterior cingulate cortex (SCC-DBS) in macaques, modeling the location in the brain proven effective for depression. We demonstrate that SCC-DBS has a selective effect on white matter macro- and micro-structure in the cingulum bundle distant to where stimulation was delivered. SCC-DBS also decreased functional connectivity between subcallosal and posterior cingulate cortex, two areas linked by the cingulum bundle and implicated in depression. Our data reveal that white matter remodeling as well as functional effects contribute to DBS's therapeutic efficacy.

Alterations in structural integrity of superior longitudinal fasciculus III associated with cognitive performance in cerebral small vessel disease

BACKGROUND

This study aimed to investigate the alterations in structural integrity of superior longitudinal fasciculus subcomponents with increasing white matter hyperintensity severity as well as the relationship to cognitive performance in cerebral small vessel disease.

METHODS

110 cerebral small vessel disease study participants with white matter hyperintensities were recruited. According to Fazekas grade scale, white matter hyperintensities of each subject were graded. All subjects were divided into two groups. The probabilistic fiber tracking method was used for analyzing microstructure characteristics of superior longitudinal fasciculus subcomponents.

RESULTS

Probabilistic fiber tracking results showed that mean diffusion, radial diffusion, and axial diffusion values of the left arcuate fasciculus as well as the mean diffusion value of the right arcuate fasciculus and left superior longitudinal fasciculus III in high white matter hyperintensities rating group were significantly higher than those in low white matter hyperintensities rating group (p < 0.05). The mean diffusion value of the left superior longitudinal fasciculus III was negatively related to the Montreal Cognitive Assessment score of study participants (p < 0.05).

CONCLUSIONS

The structural integrity injury of bilateral arcuate fasciculus and left superior longitudinal fasciculus III is more severe with the aggravation of white matter hyperintensities. The structural integrity injury of the left superior longitudinal fasciculus III correlates to cognitive impairment in cerebral small vessel disease.

Affinity of structural white matter tracts between infant and adult pig

BACKGROUND

The piglet brain has been increasingly used as an excellent surrogate for investigation of pediatric neurodevelopment, nutrition, and traumatic brain injuries. This study intends to establish a piglet brain's structural connectivity model and compare it with the adult pig, enhancing its application for structurally guided functional analysis.

METHODS

In this study, diffusion-weighted (DW)-MRI data from piglets (n=11, 3-week-old) was used to establish piglet model and compare with adult pigs. We employed a data-driven independent component analysis (ICA) method to derive piglet-specific tracts. Pearson correlations and Kullback-Leibler (KL) divergences was employed to identify common tracts and unique tracts for piglet. Common tracts were then used in a blueprint connectome study to highlight differences in regions of interest (ROI).

RESULTS

The data-driven approach applied to piglet brains revealed 17 common tracts, showing high similarity with adult pigs' white matter (WM) tracts, and identified 3 tracts unique to piglets and 10 negative marker tracts. Additionally, the study highlighted notable differences in 3 ROIs associated with blueprint connectome.

COMPARING WITH EXISTING METHODS

This study marks a significant shift from surface-based to voxel-based methodologies in analyzing pig brain structural connectivity and generating connectome blueprints. Additionally, it sheds light on the use of the piglet model for developmental studies, offering new perspectives in this area.

CONCLUSION

This study established a piglet brain tract model and conducts a comparative analysis of adult pig's and piglet's structural connectivity. These findings underscore the potential use of the piglet brain model in employing piglet model for developmental studies.

Consistent spatial lesion-symptom patterns: A comprehensive analysis using triangulation in lesion-symptom mapping in a cohort of stroke patients

INTRODUCTION

The relationship between brain lesions and stroke outcomes is crucial for advancing patient prognosis and developing effective therapies. Stroke is a leading cause of disability worldwide, and it is important to understand the neurological basis of its varied symptomatology. Lesion-symptom mapping (LSM) methods provide a means to identify brain areas that are strongly associated with specific symptoms. However, inner variations in LSM methods can yield different results. To address this, our study aimed to characterize the lesion-symptom mapping variability using three different LSM methods. Specifically, we sought to determine a lesion symptom core across LSM approaches enhancing the robustness of the analysis and removing potential spatial bias.

MATERIAL & METHODS

A cohort consisting of 35 patients with either right- or left-sided middle cerebral artery strokes were enrolled and evaluated using the NIHSS at 24 h post-stroke. Anatomical T1w MRI scans were also obtained 24 h post-stroke. Lesion masks were segmented manually and three distinctive LSM methods were implemented: ROI correlation-based, univariate, and multivariate approaches.

RESULTS

The results of the LSM analyses showed substantial spatial differences in the extension of each of the three lesion maps. However, upon overlaying all three lesion-symptom maps, a consistent lesion core emerged, corresponding to the territory associated with elevated NIHSS scores. This finding not only enhances the spatial accuracy of the lesion map but also underscores its clinical relevance.

CONCLUSION

This study underscores the significance of exploring complementary LSM approaches to investigate the association between brain lesions and stroke outcomes. By utilizing multiple methods, we can increase the robustness of our results, effectively addressing and neutralizing potential spatial bias introduced by each individual method. Such an approach holds promise for enhancing our understanding of stroke pathophysiology and optimizing patient care strategies.

Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks

Objective

In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.

Methods

Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male-female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.

Results

The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.

Conclusion

The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

White matter trajectories over the lifespan

White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant's age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials

BACKGROUND

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

NEW METHOD

Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

RESULTS

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures.

COMPARISON WITH EXISTING METHOD

Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences.

CONCLUSIONS

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Clinical applicability of automated tractography for stroke rehabilitation: Z-score conversion of fractional anisotropy

[Purpose] To expand the applicability of diffusion-tensor tractography fractional anisotropy for stroke rehabilitation, this study aimed to provide references for representative neural tracts from non-lesioned hemispheres. Therefore, we applied the assessment of neural integrity to representative stroke patients using Z-score conversion. [Participants and Methods] Fractional anisotropy values were assessed in neural tracts, including the corticospinal tract, inferior fronto-occipital fasciculus, uncinate fasciculus, and anterior thalamic radiation, of stroke patients receiving acute care. [Results] Data were collected from 60 patients for the non-lesioned right hemisphere and 68 patients for the non-lesioned left hemisphere. Mean fractional anisotropy values in the corticospinal tract and inferior fronto-occipital fasciculus were notably elevated, reaching approximately 0.6 and 0.5, respectively. The mean fractional anisotropy values for other neural tracts were approximately 0.4, and, the overall standard deviations were approximately 0.04. In two typical stroke patients assessed using Z-scores, the scores in the corticospinal tract corresponded to the severity of the hemiparesis. The scores in the anterior thalamic radiation and inferior fronto-occipital fasciculus were associated with more significant brain dysfunction, including inattention and aphasia. [Conclusion] In this study, the Z-score findings related to stroke symptoms align with those reported in the literature, indicating the appropriateness of the methodology used and its potential in future applications.

Human cortical neurogenesis is altered via glucocorticoid-mediated regulation of ZBTB16 expression

Glucocorticoids are important for proper organ maturation, and their levels are tightly regulated during development. Here, we use human cerebral organoids and mice to study the cell-type-specific effects of glucocorticoids on neurogenesis. We show that glucocorticoids increase a specific type of basal progenitors (co-expressing PAX6 and EOMES) that has been shown to contribute to cortical expansion in gyrified species. This effect is mediated via the transcription factor ZBTB16 and leads to increased production of neurons. A phenome-wide Mendelian randomization analysis of an enhancer variant that moderates glucocorticoid-induced ZBTB16 levels reveals causal relationships with higher educational attainment and altered brain structure. The relationship with postnatal cognition is also supported by data from a prospective pregnancy cohort study. This work provides a cellular and molecular pathway for the effects of glucocorticoids on human neurogenesis that relates to lasting postnatal phenotypes.

A systematic review of automated methods to perform white matter tract segmentation

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.

SARS-CoV2 evokes structural brain changes resulting in declined executive function

BACKGROUND

Several research has underlined the multi-system character of COVID-19. Though effects on the Central Nervous System are mainly discussed as disease-specific affections due to the virus' neurotropism, no comprehensive disease model of COVID-19 exists on a neurofunctional base by now. We aimed to investigate neuroplastic grey- and white matter changes related to COVID-19 and to link these changes to neurocognitive testings leading towards a multi-dimensional disease model.

METHODS

Groups of acutely ill COVID-19 patients (n = 16), recovered COVID-19 patients (n = 21) and healthy controls (n = 13) were prospectively included into this study. MR-imaging included T1-weighted sequences for analysis of grey matter using voxel-based morphometry and diffusion-weighted sequences to investigate white matter tracts using probabilistic tractography. Comprehensive neurocognitive testing for verbal and non-verbal domains was performed.

RESULTS

Alterations strongly focused on grey matter of the frontal-basal ganglia-thalamus network and temporal areas, as well as fiber tracts connecting these areas. In acute COVID-19 patients, a decline of grey matter volume was found with an accompanying diminution of white matter tracts. A decline in executive function and especially verbal fluency was found in acute patients, partially persisting in recovered.

CONCLUSION

Changes in gray matter volume and white matter tracts included mainly areas involved in networks of executive control and language. Deeper understanding of these alterations is necessary especially with respect to long-term impairments, often referred to as 'Post-COVID'.

On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations

Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS associations is stability of their feature patterns. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues systematically, we developed a generative modeling framework to simulate synthetic datasets. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the feature pattern underlying the association. We confirmed these trends across two neuroimaging modalities and in independent datasets with n ≈ 1000 and n = 20,000, and found that only the latter comprised sufficient observations for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses. Collectively, we characterize how to limit detrimental effects of overfitting on CCA/PLS stability, and provide recommendations for future studies.

Mapping the functional and structural connectivity of the scene network

The recognition and perception of places has been linked to a network of scene-selective regions in the human brain. While previous studies have focussed on functional connectivity between scene-selective regions themselves, less is known about their connectivity with other cortical and subcortical regions in the brain. Here, we determine the functional and structural connectivity profile of the scene network. We used fMRI to examine functional connectivity between scene regions and across the whole brain during rest and movie-watching. Connectivity within the scene network revealed a bias between posterior and anterior scene regions implicated in perceptual and mnemonic aspects of scene perception respectively. Differences between posterior and anterior scene regions were also evident in the connectivity with cortical and subcortical regions across the brain. For example, the Occipital Place Area (OPA) and posterior Parahippocampal Place Area (PPA) showed greater connectivity with visual and dorsal attention networks, while anterior PPA and Retrosplenial Complex showed preferential connectivity with default mode and frontoparietal control networks and the hippocampus. We further measured the structural connectivity of the scene network using diffusion tractography. This indicated both similarities and differences with the functional connectivity, highlighting biases between posterior and anterior regions, but also between ventral and dorsal scene regions. Finally, we quantified the structural connectivity between the scene network and major white matter tracts throughout the brain. These findings provide a map of the functional and structural connectivity of scene-selective regions to each other and the rest of the brain.

Design and methodology for a proof of mechanism study of individualized neuronavigated continuous Theta burst stimulation for auditory processing in adolescents with autism spectrum disorder

It has been suggested that aberrant excitation/inhibition (E/I) balance and dysfunctional structure and function of relevant brain networks may underlie the symptoms of autism spectrum disorder (ASD). However, the nomological network linking these constructs to quantifiable measures and mechanistically relating these constructs to behavioral symptoms of ASD is lacking. Herein we describe a within-subject, controlled, proof-of-mechanism study investigating the pathophysiology of auditory/language processing in adolescents with ASD. We utilize neurophysiological and neuroimaging techniques including magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG) metrics of language network structure and function. Additionally, we apply a single, individually targeted session of continuous theta burst stimulation (cTBS) as an experimental probe of the impact of perturbation of the system on these neurophysiological and neuroimaging outcomes. MRS, fMRI, and MEG measures are evaluated at baseline and immediately prior to and following cTBS over the posterior superior temporal cortex (pSTC), a region involved in auditory and language processing deficits in ASD. Also, behavioral measures of ASD and language processing and DWI measures of auditory/language network structures are obtained at baseline to characterize the relationship between the neuroimaging and neurophysiological measures and baseline symptom presentation. We hypothesize that local gamma-aminobutyric acid (GABA) and glutamate concentrations (measured with MRS), and structural and functional activity and network connectivity (measured with DWI and fMRI), will significantly predict MEG indices of auditory/language processing and behavioral deficits in ASD. Furthermore, a single session of cTBS over left pSTC is hypothesized to lead to significant, acute changes in local glutamate and GABA concentration, functional activity and network connectivity, and MEG indices of auditory/language processing. We have completed the pilot phase of the study (n=20 Healthy Volunteer adults) and have begun enrollment for the main phase with adolescents with ASD (n=86; age 14-17). If successful, this study will establish a nomological network linking local E/I balance measures to functional and structural connectivity within relevant brain networks, ultimately connecting them to ASD symptoms. Furthermore, this study will inform future therapeutic trials using cTBS to treat the symptoms of ASD.

Fibre orientation atlas guided rapid segmentation of white matter tracts

Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

Outcome Prediction by Combining Corticospinal Tract Lesion Load with Diffusion-tensor Fractional Anisotropy in Patients after Hemorrhagic Stroke

Objectives

The objective of this study was to evaluate the predictive precision of combining the corticospinal tract lesion load (CST-LL) with the diffusion-tensor fractional anisotropy of the corticospinal tract (CST-FA) in the lesioned hemispheres regarding motor outcomes.

Methods

Patients with putaminal and/or thalamic hemorrhage who had undergone computed tomography (CT) soon after onset in our hospital were retrospectively enrolled. The CST-LL was calculated after registration of the CT images to a standard brain. Diffusion-tensor imaging was performed during the second week after onset. Standardized automated tractography was employed to calculate the CST-FA. Outcomes were assessed at discharge from our affiliated rehabilitation facility using total scores of the motor component of the Stroke Impairment Assessment Set (SIAS-motor total; null to full, 0 to 25). Multivariate regression analysis was performed with CST-LL and CST-FA as explanatory variables and SIAS-motor total as a target value.

Results

Twenty-five patients participated in this study. SIAS-motor total ranged from 0 to 25 (median, 17). CST-LL ranged from 0.298 to 7.595 (median, 2.522) mL, and the lesion-side CST-FA ranged from 0.211 to 0.530 (median, 0.409). Analysis revealed that both explanatory variables were detected as statistically significant contributory factors. The estimated t values indicated that the contributions of these two variables were almost equal. The obtained regression model accounted for 63.9% of the variability of the target value.

Conclusions

Incorporation of the CST-LL with the lesion-side CST-FA enhances the precision of the stroke outcome prediction model.

Structural and functional connectivity associations with anterior cingulate sulcal variability

Background

Sulcation of the anterior cingulate may be defined by presence of a paracingulate sulcus, a tertiary sulcus developing during the third gestational trimester with implications on cognitive function and disease.

Methods

In this retrospective analysis we examine task-free resting state functional connectivity and diffusion-weighted tract segmentation data from a cohort of healthy adults (< 60-year-old, n = 129), exploring the impact of ipsilateral paracingulate sulcal presence on structural and functional connectivity.

Results

Presence of a left paracingulate sulcus was associated with reduced fractional anisotropy in the left cingulum (P = 0.02) bundle and the peri-genual (P = 0.002) and dorsal (P = 0.03) but not the temporal cingulum bundle segments. Left paracingulate sulcal presence was associated with increased left peri-genual radial diffusivity (P = 0.003) and tract volume (P = 0.012). A significant, predominantly intraregional frontal component of altered resting state functional connectivity was identified in individuals possessing a left PCS (P = 0.01). Seed-based functional connectivity in pre-defined networks was not associated with paracingulate sulcal presence.

Conclusion

These results identify a novel association between neurodevelopmentally derived sulcation and altered structural connectivity in a healthy adult population with implications for conditions where this variation is of interest. Furthermore, they provide evidence of a link between the structural and functional connectivity of the brain in the presence of a paracingulate sulcus which may be mediated by a highly connected local functional network reliant on short association fibres.

Current Auditory Hallucinations Are Not Associated With Specific White Matter Diffusion Alterations in Schizophrenia

Background and Hypothesis

Studies have linked auditory hallucinations (AH) in schizophrenia spectrum disorders (SCZ) to altered cerebral white matter microstructure within the language and auditory processing circuitry (LAPC). However, the specificity to the LAPC remains unclear. Here, we investigated the relationship between AH and DTI among patients with SCZ using diffusion tensor imaging (DTI).

Study Design

We included patients with SCZ with (AH+; n = 59) and without (AH-; n = 81) current AH, and 140 age- and sex-matched controls. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were extracted from 39 fiber tracts. We used principal component analysis (PCA) to identify general factors of variation across fiber tracts and DTI metrics. Regression models adjusted for sex, age, and age2 were used to compare tract-wise DTI metrics and PCA factors between AH+, AH-, and healthy controls and to assess associations with clinical characteristics.

Study Results

Widespread differences relative to controls were observed for MD and RD in patients without current AH. Only limited differences in 2 fiber tracts were observed between AH+ and controls. Unimodal PCA factors based on MD, RD, and AD, as well as multimodal PCA factors, differed significantly relative to controls for AH-, but not AH+. We did not find any significant associations between PCA factors and clinical characteristics.

Conclusions

Contrary to previous studies, DTI metrics differed mainly in patients without current AH compared to controls, indicating a widespread neuroanatomical distribution. This challenges the notion that altered DTI metrics within the LAPC is a specific feature underlying AH.

Cerebral blood flow and white matter alterations in adults with phenylketonuria

BACKGROUND

Phenylketonuria (PKU) represents a congenital metabolic defect that disrupts the process of converting phenylalanine (Phe) into tyrosine. Earlier investigations have revealed diminished cognitive performance and changes in brain structure and function (including the presence of white matter lesions) among individuals affected by PKU. However, there exists limited understanding regarding cerebral blood flow (CBF) and its potential associations with cognition, white matter lesions, and metabolic parameters in patients with PKU, which we therefore aimed to investigate in this study.

METHOD

Arterial spin labeling perfusion MRI was performed to measure CBF in 30 adults with early-treated classical PKU (median age 35.5 years) and 59 healthy controls (median age 30.0 years). For all participants, brain Phe levels were measured with 1H spectroscopy, and white matter lesions were rated by two neuroradiologists on T2 weighted images. White matter integrity was examined with diffusion tensor imaging (DTI). For patients only, concurrent plasma Phe levels were assessed after an overnight fasting period. Furthermore, past Phe levels were collected to estimate historical metabolic control. On the day of the MRI, each participant underwent a cognitive assessment measuring IQ and performance in executive functions, attention, and processing speed.

RESULTS

No significant group difference was observed in global CBF between patients and controls (F (1, 87) = 3.81, p = 0.054). Investigating CBF on the level of cerebral arterial territories, reduced CBF was observed in the left middle and posterior cerebral artery (MCA and PCA), with the most prominent reduction of CBF in the anterior subdivision of the MCA (F (1, 87) = 6.15, p = 0.015, surviving FDR correction). White matter lesions in patients were associated with cerebral blood flow reduction in the affected structure. Particularly, patients with lesions in the occipital lobe showed significant CBF reductions in the left PCA (U = 352, p = 0.013, surviving FDR correction). Additionally, axial diffusivity measured with DTI was positively associated with CBF in the ACA and PCA (surviving FDR correction). Cerebral blood flow did not correlate with cognitive performance or metabolic parameters.

CONCLUSION

The relationship between cerebral blood flow and white matter indicates a complex interplay between vascular health and white matter alterations in patients with PKU. It highlights the importance of considering a multifactorial model when investigating the impact of PKU on the brain.

Subject-Specific Automatic Reconstruction of White Matter Tracts

MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In this work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test-retest reproducibility) of tractography results of six clinically relevant white matter tracts by using healthy volunteer data (N = 136) from the Human Connectome Project consortium. A deep learning convolutional network-based approach was used for individualized segmentation of regions of interest, combined with an evidence-based tractography protocol and appropriate post-tractography filtering. Robustness was evaluated by estimating the consistency of tractography probability maps, i.e., averaged tractograms in normalized space, through the use of a hold-out cross-validation approach. No major outliers were found, indicating a high robustness of the tractography results. Reliability was evaluated at the individual level. First by examining the overlap of tractograms that resulted from repeatedly processed identical MRI scans (N = 10, 10 iterations) to establish an upper limit of reliability of the pipeline. Second, by examining the overlap for subjects that were scanned twice at different time points (N = 40). Both analyses indicated high reliability, with the second analysis showing a reliability near the upper limit. The robust and reliable subject-specific generation of white matter tracts in healthy subjects holds promise for future validation of our pipeline in a clinical population and subsequent implementation in brain tumor surgery.

Applicability of fractional anisotropy from standardized automated tractography for outcome prediction of patients after stroke

[Purpose] Diffusion-tensor fractional anisotropy has been used for outcome prediction in stroke patients. We assessed the clinical applicability of the two major fractional anisotropy methodologies-fractional anisotropy derived from segmentation maps in the standard brain (region of interest) and fractional anisotropy derived from standardized automated tractography-in relation to outcomes. [Participants and Methods] The study design was a retrospective survey of medical records collected from October 2021 to September 2022. Diffusion-tensor imaging was conducted in the second week after stroke onset. Outcomes were assessed using the total score of the motor component of the Stroke Impairment Assessment Set (null to full, 0 to 25). Correlations between fractional anisotropy and the outcomes were then assessed. [Results] Fourteen patients with hemorrhagic stroke were sampled. The fractional anisotropy from standardized automated tractography of the corticospinal tract on the lesion side (mean ± standard deviation, 0.403 ± 0.070) was significantly and tightly correlated (r=0.813) with the outcomes (13.4 ± 9.2), whereas the fractional anisotropy from a region of interest set in the cerebral peduncle on the lesion side (0.548 ± 0.064) was not significantly correlated with the outcomes (r=0.507). [Conclusion] The findings suggest that fractional anisotropy derived from standardized automated tractography can be more applicable to outcome prediction than that derived from a region of interest defined in the standard brain.

Automated Tractography for the Assessment of Aphasia in Acute Care Stroke Rehabilitation: A Case Series

Background

Aphasia is a common disorder among stroke patients. Assessment of aphasia is essential for scheduling appropriate rehabilitative treatment. Although this is conventionally accomplished using neuropsychological test batteries, these tests are not always accessible because of attention and/or consciousness disturbances during acute care. To overcome this issue, we have introduced a newly developed automated tractography known as XTRACT.

Cases

Diffusion-tensor images were acquired from three patients on days 10-14. Brain images were processed by XTRACT, which automatically extracts neural tracts using standardized protocols. Fractional anisotropy (FA) values were then bilaterally evaluated in the following neural tracts associated with aphasia: arcuate fasciculus, inferior fronto-occipital fasciculus, middle longitudinal fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus. Case 1 had word-finding difficulty on admission. FA values in the lesioned left hemisphere were not decreased in all tracts and this patient fully recovered during acute care. Case 2 had reduced spontaneous speech and a low FA value in the left arcuate fasciculus. Rehabilitative treatment was scheduled to improve the verbal output of sentences and word recall. Case 3 could not complete the conventional aphasia test battery because of attention disturbance. He had low FA values in all tracts in the left hemisphere. Rehabilitative treatment was designed to focus on both speaking and auditory comprehension.

Discussion

Automated tractography enables quantitative assessment of the neural damage associated with aphasia, even in patients with attention and/or consciousness disturbances. This modality can aid in the assessment of aphasia and allows the planning of appropriate rehabilitative treatment.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials

Background

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

New Method

Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

Results

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures.

Comparison with existing methods

Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences.

Conclusions

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Structural connectivity of the multiple demand network in humans and comparison to the macaque brain

Fluid intelligence encompasses a wide range of abilities such as working memory, problem-solving, and relational reasoning. In the human brain, these abilities are associated with the Multiple Demand Network, traditionally thought to involve combined activity of specific regions predominantly in the prefrontal and parietal cortices. However, the structural basis of the interactions between areas in the Multiple Demand Network, as well as their evolutionary basis among primates, remains largely unexplored. Here, we exploit diffusion MRI to elucidate the major white matter pathways connecting areas of the human core and extended Multiple Demand Network. We then investigate whether similar pathways can be identified in the putative homologous areas of the Multiple Demand Network in the macaque monkey. Finally, we contrast human and monkey networks using a recently proposed approach to compare different species' brains within a common organizational space. Our results indicate that the core Multiple Demand Network relies mostly on dorsal longitudinal connections and, although present in the macaque, these connections are more pronounced in the human brain. The extended Multiple Demand Network relies on distinct pathways and communicates with the core Multiple Demand Network through connections that also appear enhanced in the human compared with the macaque.

DENOISING DIFFUSION MRI: CONSIDERATIONS AND IMPLICATIONS FOR ANALYSIS

Development of diffusion MRI (dMRI) denoising approaches has experienced considerable growth over the last years. As noise can inherently reduce accuracy and precision in measurements, its effects have been well characterised both in terms of uncertainty increase in dMRI-derived features and in terms of biases caused by the noise floor, the smallest measurable signal given the noise level. However, gaps in our knowledge still exist in objectively characterising dMRI denoising approaches in terms of both of these effects and assessing their efficacy. In this work, we reconsider what a denoising method should and should not do and we accordingly define criteria to characterise the performance. We propose a comprehensive set of evaluations, including i) benefits in improving signal quality and reducing noise variance, ii) gains in reducing biases and the noise floor and improving, iii) preservation of spatial resolution, iv) agreement of denoised data against a gold standard, v) gains in downstream parameter estimation (precision and accuracy), vi) efficacy in enabling noise-prone applications, such as ultra-high-resolution imaging. We further provide newly acquired complex datasets (magnitude and phase) with multiple repeats that sample different SNR regimes to highlight performance differences under different scenarios. Without loss of generality, we subsequently apply a number of exemplar patch-based denoising algorithms to these datasets, including Non-Local Means, Marchenko-Pastur PCA (MPPCA) in the magnitude and complex domain and NORDIC, and compare them with respect to the above criteria and against a gold standard complex average of multiple repeats. We demonstrate that all tested denoising approaches reduce noise-related variance, but not always biases from the elevated noise floor. They all induce a spatial resolution penalty, but its extent can vary depending on the method and the implementation. Some denoising approaches agree with the gold standard more than others and we demonstrate challenges in even defining such a standard. Overall, we show that dMRI denoising performed in the complex domain is advantageous to magnitude domain denoising with respect to all the above criteria.

FIESTA: Autoencoders for accurate fiber segmentation in tractography

White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework.

Cingulate dynamics track depression recovery with deep brain stimulation

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.

An Individualized Tractography Pipeline for the Nucleus Basalis of Meynert Lateral Tract

Background

At the center of the cortical cholinergic network, the nucleus basalis of Meynert (NBM) is crucial for the cognitive domains most vulnerable in PD. Preclinical evidence has demonstrated the positive impact of NBM deep brain stimulation (DBS) on cognition but early human trials have had mixed results. It is possible that DBS of the lateral NBM efferent white matter fiber bundle may be more effective at improving cognitive-motor function. However, precise tractography modelling is required to identify the optimal target for neurosurgical planning. Individualized tractography approaches have been shown to be highly effective for accurately identifying DBS targets but have yet to be developed for the NBM.

Methods

Using structural and diffusion weighted imaging, we developed a tractography pipeline for precise individualized identification of the lateral NBM target tract. Using dice similarity coefficients, the reliability of the tractography outputs was assessed across three cohorts to investigate: 1) whether this manual pipeline is more reliable than an existing automated pipeline currently used in the literature; 2) the inter- and intra-rater reliability of our pipeline in research scans of patients with PD; and 3) the reliability and practicality of this pipeline in clinical scans of DBS patients.

Results

The individualized manual pipeline was found to be significantly more reliable than the existing automated pipeline for both the segmentation of the NBM region itself (p<0.001) and the reconstruction of the target lateral tract (p=0.002). There was also no significant difference between the reliability of two different raters in the PD cohort (p=0.25), which showed high inter- (mean Dice coefficient >0.6) and intra-rater (mean Dice coefficient >0.7) reliability across runs. Finally, the pipeline was shown to be highly reliable within the clinical scans (mean Dice coefficient = 0.77). However, accurate reconstruction was only evident in 7/10 tracts.

Conclusion

We have developed a reliable tractography pipeline for the identification and analysis of the NBM lateral tract in research and clinical grade imaging of healthy young adult and PD patient scans.

No laughing white matter: Reduced integrity of the cortical cholinergic pathways in Parkinson's disease-related cognitive impairment

BACKGROUND

Approximately one third of recently diagnosed Parkinson's disease (PD) patients experience cognitive decline. The nucleus basalis of Meynert (NBM) degenerates early in PD and is crucial for cognitive function. The two main NBM white matter pathways include a lateral and medial trajectory. However, research is needed to determine which pathway, if any, are associated with PD-related cognitive decline.

METHODS

Thirty-seven PD patients with no mild cognitive impairment (MCI) were included in this study. Participants either developed MCI at 1-year follow up (PD MCI-Converters; n = 16) or did not (PD no-MCI; n = 21). Mean diffusivity (MD) and fractional anisotropy (FA) of the medial and lateral NBM tracts were extracted using probabilistic tractography. Between-group differences in MD and FA for each tract was compared using ANCOVA, controlling for age, sex, and disease duration. Control comparisons of the internal capsule MD and FA were also performed. Associations between baseline MD or FA and cognitive outcomes (working memory, psychomotor speed, delayed recall, and visuospatial function) were assessed using linear mixed models.

RESULTS

PD MCI-Converters had significantly greater MD and lower FA (p < .001) of both NBM tracts compared to PD no-MCI. No difference was found in the MD (p = .06) or FA (p = .31) of the control region. Trends were identified between: 1) lateral tract MD and FA with working memory decline; and 2) medial tract MD and reduced psychomotor speed.

CONCLUSIONS

Reduced integrity of the NBM tracts is evident in PD patients up to one year prior to the development of MCI. Thus, deterioration of the NBM tracts in PD may be an early marker of those at risk of cognitive decline.

Relating white matter microstructure in theoretically defined addiction networks to relapse in alcohol use disorder

Theoretical models group maladaptive behaviors in addiction into neurocognitive domains such as incentive salience (IS), negative emotionality (NE), and executive functioning (EF). Alterations in these domains lead to relapse in alcohol use disorder (AUD). We examine whether microstructural measures in the white matter pathways supporting these domains are associated with relapse in AUD. Diffusion kurtosis imaging data were collected from 53 individuals with AUD during early abstinence. We used probabilistic tractography to delineate the fornix (IS), uncinate fasciculus (NE), and anterior thalamic radiation (EF) in each participant and extracted mean fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) within each tract. Binary (abstained vs. relapsed) and continuous (number of days abstinent) relapse measures were collected over a 4-month period. Across tracts, anisotropy measures were typically (i) lower in those that relapsed during the follow-up period and (ii) positively associated with the duration of sustained abstinence during the follow-up period. However, only KFA in the right fornix reached significance in our sample. The association between microstructural measures in these fiber tracts and treatment outcome in a small sample highlights the potential utility of the three-factor model of addiction and the role of white matter alterations in AUD.