A Narrative Review on the Clinical Relevance of Imaging the Circumventricular Brain Organs and Performing Their Anatomical and Histopathological Examination in Acute and Postacute COVID-19.

ABSTRACT: Autopsy followed by histopathological examination is foundational in clinical and forensic medicine for discovering and understanding pathological changes in disease, their underlying processes, and cause of death. Imaging technology has become increasingly important for advancing clinical research and practice, given its noninvasive, in vivo and ex vivo applicability. Medical and forensic autopsy can benefit greatly from advances in imaging technology that lead toward minimally invasive, whole-brain virtual autopsy. Brain autopsy followed by histopathological examination is still the hallmark for understanding disease and a fundamental modus operandi in forensic pathology and forensic medicine, despite the fact that its practice has become progressively less frequent in medical settings. This situation is especially relevant with respect to new diseases such as COVID-19 caused by the SARS-CoV-2 virus, for which our neuroanatomical knowledge is sparse. In this narrative review, we show that ad hoc clinical autopsies and histopathological analyses combined with neuroimaging of the principal circumventricular organs are critical to gaining insight into the reconstruction of the pathophysiological mechanisms and the explanation of cause of death (ie, atrium mortis) related to the cardiovascular effects of SARS-CoV-2 infection in forensic and clinical medicine.

Investigating the structural network underlying brain-immune interactions using combined histopathology and neuroimaging: a critical review for its relevance in acute and long COVID-19.

Current views on immunity support the idea that immunity extends beyond defense functions and is tightly intertwined with several other fields of biology such as virology, microbiology, physiology and ecology. It is also critical for our understanding of autoimmunity and cancer, two topics of great biological relevance and for critical public health considerations such as disease prevention and treatment. Central to this review, the immune system is known to interact intimately with the nervous system and has been recently hypothesized to be involved not only in autonomic and limbic bio-behaviors but also in cognitive function. Herein we review the structural architecture of the brain network involved in immune response. Furthermore, we elaborate upon the implications of inflammatory processes affecting brain-immune interactions as reported recently in pathological conditions due to SARS-Cov-2 virus infection, namely in acute and post-acute COVID-19. Moreover, we discuss how current neuroimaging techniques combined with ad hoc clinical autopsies and histopathological analyses could critically affect the validity of clinical translation in studies of human brain-immune interactions using neuroimaging. Advances in our understanding of brain-immune interactions are expected to translate into novel therapeutic avenues in a vast array of domains including cancer, autoimmune diseases or viral infections such as in acute and post-acute or Long COVID-19.

Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction.

Shape plays an important role in computer graphics, offering informative features to convey an object's morphology and functionality. Shape analysis in brain imaging can help interpret structural and functionality correlations of the human brain. In this work, we investigate the shape of the brain's 3D white matter connections and its potential predictive relationship to human cognitive function. We reconstruct brain connections as sequences of 3D points using diffusion magnetic resonance imaging (dMRI) tractography. To describe each connection, we extract 12 shape descriptors in addition to traditional dMRI connectivity and tissue microstructure features. We introduce a novel framework, Shape--fused Fiber Cluster Transformer (SFFormer), that leverages a multi-head cross-attention feature fusion module to predict subject-specific language performance based on dMRI tractography. We assess the performance of the method on a large dataset including 1065 healthy young adults. The results demonstrate that both the transformer-based SFFormer model and its inter/intra feature fusion with shape, microstructure, and connectivity are informative, and together, they improve the prediction of subject-specific language performance scores. Overall, our results indicate that the shape of the brain's connections is predictive of human language function.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

More organized white matter is associated with positivity bias in older adults.

On average, healthy older adults prefer positive over neutral or negative stimuli. This positivity bias is related to memory and attention processes and is linked to the function and structure of several interconnected brain areas. However, the relationship between the positivity bias and white matter integrity remains elusive. The present study examines how white matter organization relates to the degree of the positivity bias among older adults. We collected imaging and behavioral data from 25 individuals (12 females, 13 males, and a mean age of 77.32). Based on a functional memory task, we calculated a Pos-Neg score, reflecting the memory for positively valenced information over negative information, and a Pos-Neu score, reflecting the memory for positively valenced information over neutral information. Diffusion-weighted magnetic resonance imaging data were processed using Tract-Based Spatial Statistics. We performed two non-parametric permutation tests to correlate whole brain white matter integrity and the Pos-Neg and Pos-Neu scores while controlling for age, sex, and years of education. We observed a statistically significant positive association between the Pos-Neu score and white matter integrity in multiple brain connections, mostly frontal. The results did not remain significant when including verbal episodic memory as an additional covariate. Our study indicates that the positivity bias in memory in older adults is associated with more organized white matter in the connections of the frontal brain. While these frontal areas are critical for memory and executive processes and have been related to pathological aging, more extensive studies are needed to fully understand their role in the positivity bias and the potential for therapeutic interventions.

Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning.

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

Brains of endurance athletes differ in the association areas but not in the primary areas.

Regular participation in sports results in a series of physiological adaptations. However, little is known about the brain adaptations to physical activity. Here we aimed to investigate whether young endurance athletes and non-athletes differ in the gray and white matter of the brain and whether cardiorespiratory fitness (CRF) is associated with these differences. We assessed the CRF, volumes of the gray and white matter of the brain using structural magnetic resonance imaging (sMRI), and brain white matter connections using diffusion magnetic resonance imaging (dMRI) in 20 young male endurance athletes and 21 healthy non-athletes. While total brain volume was similar in both groups, the white matter volume was larger and the gray matter volume was smaller in the athletes compared to non-athletes. The reduction of gray matter was located in the association areas of the brain that are specialized in processing of sensory stimuli. In the microstructure analysis, significant group differences were found only in the association tracts, for example, the inferior occipito-frontal fascicle (IOFF) showing higher fractional anisotropy and lower radial diffusivity, indicating stronger myelination in this tract. Additionally, gray and white matter brain volumes, as well as association tracts correlated with CRF. No changes were observed in other brain areas or tracts. In summary, the brain signature of the endurance athlete is characterized by changes in the integration of sensory and motor information in the association areas.

Structural connectivity of cytoarchitectonically distinct human left temporal pole subregions: a diffusion MRI tractography study.

The temporal pole (TP) is considered one of the major paralimbic cortical regions, and is involved in a variety of functions such as sensory perception, emotion, semantic processing, and social cognition. Based on differences in cytoarchitecture, the TP can be further subdivided into smaller regions (dorsal, ventrolateral and ventromedial), each forming key nodes of distinct functional networks. However, the brain structural connectivity profile of TP subregions is not fully clarified. Using diffusion MRI data in a set of 31 healthy subjects, we aimed to elucidate the comprehensive structural connectivity of three cytoarchitectonically distinct TP subregions. Diffusion tensor imaging (DTI) analysis suggested that major association fiber pathways such as the inferior longitudinal, middle longitudinal, arcuate, and uncinate fasciculi provide structural connectivity to the TP. Further analysis suggested partially overlapping yet still distinct structural connectivity patterns across the TP subregions. Specifically, the dorsal subregion is strongly connected with wide areas in the parietal lobe, the ventrolateral subregion with areas including constituents of the default-semantic network, and the ventromedial subregion with limbic and paralimbic areas. Our results suggest the involvement of the TP in a set of extensive but distinct networks of cortical regions, consistent with its functional roles.

A proposed structural connectivity matrices approach for the superior fronto-occipital fascicle in the Harvard-Oxford Atlas comparative framework following the Pandya comparative extrapolation principle.

A key set of connections necessary for the most complex brain functions are the long association cortico-cortical fiber tracts. These pathways have been described by the Dejerines and others using post mortem histological or brain dissection techniques. Given methodological limitations, these fiber connections have not been delineated completely in humans. Although the stem portions of fiber tracts have been identified in humans, their precise origins and terminations remain to be determined. By contrast, the origins and terminations as well as the stems of long cortico-cortical association fiber pathways in monkeys have been detailed in the macaque monkey brain using experimental tract tracing methods. Deepak Pandya made major contributions to the delineation of fiber tracts in the monkey brain. In the early 1990s, he compared his observations in monkeys with the original descriptions in humans by the Dejerines. With the advent of diffusion-weighted imaging, Dr. Pandya extended this line of investigation to the human brain with Dr. Nikos Makris. In this translational analysis of long association cortico-cortical fiber tracts, they applied a principle of extrapolation from monkey to human. In the present study, we addressed the reasoning and the complex methodology in translating brain structural connectivity from monkey to human in one cortico-cortical fiber tract, namely the superior fronto-occipital fascicle, which was delineated in both species by Dr. Pandya and colleagues. Furthermore, we represented this information in the form of connectional matrices in the context of the HOA2.0-ComPaRe framework, a homological monkey-to-human translational system used in neuroimaging.

Clustering and Switching in Semantic Verbal Fluency: Their Development and Relationship with Word Productivity in Typically Developing Greek-Speaking Children and Adolescents.

Performance in semantic verbal fluency (SVF) tasks, mainly measured by the number of words of a particular semantic category produced within a limited time, is a widely accepted measure of cognitive functioning used in the neuropsychological assessment of children and adults. Two strategic processes, Clustering and Switching (C&S) have been proposed to underlie fluency processes and affect performance in the task. However, few studies have reported on the development of those cognitive strategies and their relationship with word productivity in typically developing children. Even fewer studies have covered a broad developmental period from preschool to adolescence or measured the effect of contextual factors in this relationship. Based on a sample of 472 typically developing Greek-speaking children aged 4;0 to 16;11 years, we investigated the development of SVF performance and reported on the degree to which it is affected by C&S strategies, children's sex, and level of parental education. Results revealed a large effect of age on word productivity and on the use of C&S strategies. Two switching factors (number of clusters and number of switches) and two clustering factors (mean cluster size and a novel measure, maximum cluster size), appeared to be significantly associated with word productivity, with the largest effect being attributed to the two switching factors. C&S factors, together with children's age and parental education, predicted 91.7% of the variance in the SVF score. Children's sex was not found to have a significant effect on either word productivity or C&S strategies. Results are discussed for their theoretical implications on the strategic processes underlying word production in typically developing children.

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods.

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

From 'mental fog' to post-acute COVID-19 syndrome's executive function alteration: Implications for clinical approach.

A common symptom of the neuropsychiatric Post-Acute COVID-19 syndrome (neuro-PACS) is the so called 'brain fog'. Patients describe the brain fog as problems with attention, memory and mental fatigue. Brain fog is experienced by 9-55% of people for months after having contracted SARS-CoV-2 virus. Several theories have been proposed to explain PACS's brain fog, including a neuroinflammatory hypothesis, but the hypothesis remains to be proven. Here, we examined inflammatory and immunological blood profile in a cohort of patients with PACS to investigate the association between executive functions and blood inflammatory markers. Executive function was assessed by the Trail Making Test (TMT) Part A and Part B, as well as the Barkley Deficits in Executive Functioning Scale (BDEFS), in 71 patients (36 men), average age of 40 years (range: 15-82, SD: 15.7). Impairment in executive functioning (BDEFS scores and TMT B scores) correlated with increased levels of Interleukin-6 (IL-6), fibrinogen and ferritin. Moreover, elevated levels of Il-6, fibrinogen, ferritin, tumor necrosis factor-alpha and C-reactive protein have been observed in PACS. These findings demonstrate that PACS is characterized by the presence of an immuno-inflammatory process, which is associated with diminished executive functioning. Here, we argue in favour of a shift from the non-descriptive definition of 'mental fog' to a characterization of a subtype of PACS, associated with alteration in executive functioning. Implication for clinical settings and prevention are discussed.

Investigation of brain iron in anorexia nervosa, a quantitative susceptibility mapping study.

BACKGROUND: Anorexia nervosa (AN) is a potentially fatal psychiatric condition, associated with structural brain changes such as gray matter volume loss. The pathophysiological mechanisms for these changes are not yet fully understood. Iron is a crucial element in the development and function of the brain. Considering the systemic alterations in iron homeostasis in AN, we hypothesized that brain iron would be altered as a possible factor associated with structural brain changes in AN. METHODS: In this study, we used quantitative susceptibility mapping (QSM) magnetic resonance imaging to investigate brain iron in current AN (c-AN) and weight-restored AN compared with healthy individuals. Whole-brain voxel wise comparison was used to probe areas with possible group differences. Further, the thalamus, caudate nucleus, putamen, nucleus accumbens, hippocampus, and amygdala were selected as the regions of interest (ROIs) for ROI-based comparison of mean QSM values. RESULTS: Whole-brain voxel-wise and ROI-based comparison of QSM did not reveal any differences between groups. Exploratory analyses revealed a correlation between higher regional QSM (higher iron) and lower body mass index, higher illness severity, longer illness duration, and younger age at onset in the c-AN group. CONCLUSIONS: This study did not find evidence of altered brain iron in AN compared to healthy individuals. However, the correlations between clinical variables and QSM suggest a link between brain iron and weight status or biological processes in AN, which warrants further investigation.

Exploring the impact of hippocampal sclerosis on white matter tracts and memory in individuals with mesial temporal lobe epilepsy.

OBJECTIVE: To investigate how the presence/side of hippocampal sclerosis (HS) are related to the white matter structure of cingulum bundle (CB), arcuate fasciculus (AF), and inferior longitudinal fasciculus (ILF) in mesial temporal lobe epilepsy (MTLE). METHODS: We acquired diffusion-weighted magnetic resonance imaging (MRI) from 86 healthy and 71 individuals with MTLE (22 righ-HS; right-HS, 34 left-HS; left-HS, and 15 nonlesional MTLE). We utilized two-tensor tractography and fiber clustering to compare fractional anisotropy (FA) of each side/tract between groups. Additionally, we examined the association between FA and nonverbal (WMS-R) and verbal (WMS-R, RAVLT codification) memory performance for MTLE individuals. RESULTS: White matter abnormalities depended on the side and presence of HS. The left-HS demonstrated widespread abnormalities for all tracts, the right-HS showed lower FA for ipsilateral tracts and the nonlesional MTLE group did not differ from healthy individuals. Results indicate no differences in verbal/nonverbal memory performance between the groups, but trend-level associations between higher FA of visual memory and the left CB (r = 0.286, P = 0.018), verbal memory (RAVLT) and -left CB (r = 0.335, P = 0.005), -right CB (r = 0.286, P = 0.016), and -left AF (r = 0.287, P = 0.017). SIGNIFICANCE: Our results highlight that the presence and side of HS are crucial to understand the pathophysiology of MTLE. Specifically, left-sided HS seems to be related to widespread bilateral white matter abnormalities. Future longitudinal studies should focus on developing diagnostic and treatment strategies dependent on HS's presence/side.

Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation.

White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) study has collected data from over 10,000 children across 21 sites, providing valuable insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a database of harmonized and processed ABCD dMRI data has been created, comprising quality-controlled imaging data from 9345 subjects. This resource required significant computational effort, taking ~50,000 CPU hours to harmonize the data, perform white matter parcellation, and run whole brain tractography. The database includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts both in full-resolution (for analysis) and low-resolution (for visualization), and 804 different dMRI-derived measures per subject. It is available via the NIMH Data Archive and offers tremendous potential for scientific discoveries in structural connectivity studies of neurodevelopment in children and adolescents. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

A Proposed Human Structural Brain Connectivity Matrix in the Center for Morphometric Analysis Harvard-Oxford Atlas Framework: A Historical Perspective and Future Direction for Enhancing the Precision of Human Structural Connectivity with a Novel Neuroanatomical Typology.

A complete structural definition of the human nervous system must include delineation of its wiring diagram (e.g., Swanson LW. Brain architecture: understanding the basic plan, 2012). The complete formulation of the human brain circuit diagram (BCD [Front Neuroanat. 2020;14:18]) has been hampered by an inability to determine connections in their entirety (i.e., not only pathway stems but also origins and terminations). From a structural point of view, a neuroanatomic formulation of the BCD should include the origins and terminations of each fiber tract as well as the topographic course of the fiber tract in three dimensions. Classic neuroanatomical studies have provided trajectory information for pathway stems and their speculative origins and terminations [Dejerine J and Dejerine-Klumpke A. Anatomie des Centres Nerveux, 1901; Dejerine J and Dejerine-Klumpke A. Anatomie des Centres Nerveux: Méthodes générales d'étude-embryologie-histogénèse et histologie. Anatomie du cerveau, 1895; Ludwig E and Klingler J. Atlas cerebri humani, 1956; Makris N. Delineation of human association fiber pathways using histologic and magnetic resonance methodologies; 1999; Neuroimage. 1999 Jan;9(1):18-45]. We have summarized these studies previously [Neuroimage. 1999 Jan;9(1):18-45] and present them here in a macroscale-level human cerebral structural connectivity matrix. A matrix in the present context is an organizational construct that embodies anatomical knowledge about cortical areas and their connections. This is represented in relation to parcellation units according to the Harvard-Oxford Atlas neuroanatomical framework established by the Center for Morphometric Analysis at Massachusetts General Hospital in the early 2000s, which is based on the MRI volumetrics paradigm of Dr. Verne Caviness and colleagues [Brain Dev. 1999 Jul;21(5):289-95]. This is a classic connectional matrix based mainly on data predating the advent of DTI tractography, which we refer to as the "pre-DTI era" human structural connectivity matrix. In addition, we present representative examples that incorporate validated structural connectivity information from nonhuman primates and more recent information on human structural connectivity emerging from DTI tractography studies. We refer to this as the "DTI era" human structural connectivity matrix. This newer matrix represents a work in progress and is necessarily incomplete due to the lack of validated human connectivity findings on origins and terminations as well as pathway stems. Importantly, we use a neuroanatomical typology to characterize different types of connections in the human brain, which is critical for organizing the matrices and the prospective database. Although substantial in detail, the present matrices may be assumed to be only partially complete because the sources of data relating to human fiber system organization are limited largely to inferences from gross dissections of anatomic specimens or extrapolations of pathway tracing information from nonhuman primate experiments [Front Neuroanat. 2020;14:18, Front Neuroanat. 2022;16:1035420, and Brain Imaging Behav. 2021;15(3):1589-1621]. These matrices, which embody a systematic description of cerebral connectivity, can be used in cognitive and clinical studies in neuroscience and, importantly, to guide research efforts for further elucidating, validating, and completing the human BCD [Front Neuroanat. 2020;14:18].

A preliminary choroid plexus volumetric study in individuals with psychosis.

The choroid plexus (ChP) is part of the blood-cerebrospinal fluid barrier, regulating brain homeostasis and the brain's response to peripheral events. Its upregulation and enlargement are considered essential in psychosis. However, the timing of the ChP enlargement has not been established. This study introduces a novel magnetic resonance imaging-based segmentation method to examine ChP volumes in two cohorts of individuals with psychosis. The first sample consists of 41 individuals with early course psychosis (mean duration of illness = 1.78 years) and 30 healthy individuals. The second sample consists of 30 individuals with chronic psychosis (mean duration of illness = 7.96 years) and 34 healthy individuals. We utilized manual segmentation to measure ChP volumes. We applied ANCOVAs to compare normalized ChP volumes between groups and partial correlations to investigate the relationship between ChP, LV volumes, and clinical characteristics. Our segmentation demonstrated good reliability (.87). We further showed a significant ChP volume increase in early psychosis (left: p < .00010, right: p < .00010) and a significant positive correlation between higher ChP and higher LV volumes in chronic psychosis (left: r = .54, p = .0030, right: r = .68; p < .0010). Our study suggests that ChP enlargement may be a marker of acute response around disease onset. It might also play a modulatory role in the chronic enlargement of lateral ventricles, often reported in psychosis. Future longitudinal studies should investigate the dynamics of ChP enlargement as a promising marker for novel therapeutic strategies.