Human Connectome Project Database Research Articles

An Attention-Based Hemispheric Relation Inference Network for Perinatal Brain Age Prediction.

Brain anatomical age is an effective feature to assess the status of the brain, such as atypical development and aging. Although some deep learning models have been developed for estimating infant brain age, the performance of these models was unsatisfactory because few of them considered the developmental characteristics of brain anatomy during the perinatal period-the most rapid and complex developmental stage across the lifespan. The present study proposed an attention-based hemispheric relation inference network (HRINet) that takes advantage of the nature of brain structural lateralization during early development. This model captures the inter-hemispheric relationship using a graph attention mechanism and transmits lateralization information as features to describe the interactive development between bilateral hemispheres. The HRINet was used to estimate the brain age of 531 preterm and full-term neonates from the Developing Human Connectome Project (dHCP) database based on two metrics (mean curvature and sulcal depth) characterizing the folding morphology of the cortex. Our results showed that the HRINet outperformed other benchmark models in fitting the perinatal brain age, with mean absolute error of 0.53 and determination coefficient of 0.89. We also verified the generalizability of the HRINet on an extra independent dataset collected from the Gansu Provincial Maternity and Child-care Hospital. Furthermore, by applying the best-performing model to an independent dataset consisting of 47 scans of preterm infants at term-equivalent age, we showed that the predicted age was significantly lower than the chronological age, suggesting a delayed development of premature brains. Our results demonstrate the effectiveness and generalizability of the HRINet in estimating infant brain age, providing promising clinical applications for assessing neonatal brain maturity.

No sex difference in maturation of brain morphology during the perinatal period.

Accumulating evidence have documented sex differences in brain anatomy from early childhood to late adulthood. However, whether sex difference of brain structure emerges in the neonatal brain and how sex modulates the development of cortical morphology during the perinatal stage remains unclear. Here, we utilized T2-weighted MRI from the Developing Human Connectome Project (dHCP) database, consisting of 41 male and 40 female neonates born between 35 and 43 postmenstrual weeks (PMW). Neonates of each sex were arranged in a continuous ascending order of age to capture the progressive changes in cortical thickness and curvature throughout the developmental continuum. The maturational covariance network (MCN) was defined as the coupled developmental fluctuations of morphology measures between cortical regions. We constructed MCNs based on the two features, respectively, to illustrate their developmental interdependencies, and then compared the network topology between sexes. Our results showed that cortical structural development exhibited a localized pattern in both males and females, with no significant sex differences in the developmental trajectory of cortical morphology, overall organization, nodal importance, and modular structure of the MCN. Furthermore, by merging male and female neonates into a unified cohort, we identified evident dependencies influences in structural development between different brain modules using the Granger causality analysis (GCA), emanating from high-order regions toward primary cortices. Our findings demonstrate that the maturational pattern of cortical morphology may not differ between sexes during the perinatal period, and provide evidence for the developmental causality among cortical structures in perinatal brains.

The functional connectivity of the human claustrum, according to the Human Connectome Project database.

The claustrum is an irregular and fine sheet of grey matter in the basolateral telencephalon present in almost all mammals. The claustrum has been the object of several studies using animal models and, more recently, in human beings using neuroimaging. One of the most extended cognitive processes attributed to the claustrum is the salience process, which is also related to the insular cortex. In the same way, studies with human subjects and functional magnetic resonance imaging have reported the coactivation of the claustrum/insular cortex in the integration of sensory signals. This coactivation has been reported in the left claustrum/insular cortex or in the right claustrum/insular cortex. The asymmetry has been reported in task studies and literature related to neurological disorders such as Alzheimer's disease and schizophrenia, relating the severity of delusions with the reduction in left claustral volume. We present a functional connectivity study of the claustrum. Resting-state functional and anatomical MRI data from 100 healthy subjects were analyzed; taken from the Human Connectome Project (HCP, NIH Blueprint: The Human Connectome Project), with 2x2x2 mm3 voxel resolution. We hypothesize that 1) the claustrum is a node involved in different brain networks, 2) the functional connectivity pattern of the claustrum is different from the insular cortex's pattern, and 3) the asymmetry is present in the claustrum's functional connectivity. Our findings include at least three brain networks related to the claustrum. We found functional connectivity between the claustrum, frontoparietal network, and the default mode network as a distinctive attribute. The functional connectivity between the right claustrum with the frontoparietal network and the dorsal attention network supports the hypothesis of claustral asymmetry. These findings provide functional evidence, suggesting that the claustrum is coupled with the frontoparietal network serving together to instantiate new task states by flexibly modulating and interacting with other control and processing networks.

Weighting the structural connectome: Exploring its impact on network properties and predicting cognitive performance in the human brain.

Brain function does not emerge from isolated activity, but rather from the interactions and exchanges between neural elements that form a network known as the connectome. The human connectome consists of structural and functional aspects. The structural connectome (SC) represents the anatomical connections, and the functional connectome represents the resulting dynamics that emerge from this arrangement of structures. As there are different ways of weighting these connections, it is important to consider how such different approaches impact study conclusions. Here, we propose that different weighted connectomes result in varied network properties, and while neither superior the other, selection might affect interpretation and conclusions in different study cases. We present three different weighting models, namely, number of streamlines (NOS), fractional anisotropy (FA), and axon diameter distribution (ADD), to demonstrate these differences. The later, is extracted using recently published AxSI method and is first compared to commonly used weighting methods. Moreover, we explore the functional relevance of each weighted SC, using the Human Connectome Project (HCP) database. By analyzing intelligence-related data, we develop a predictive model for cognitive performance based on graph properties and the National Institutes of Health (NIH) toolbox. Results demonstrate that the ADD SC, combined with a functional subnetwork model, outperforms other models in estimating cognitive performance.

GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

A revision of the dorsal origin of the frontal aslant tract (FAT) in the superior frontal gyrus: a DWI-tractographic study.

The frontal aslant tract (FAT) is a white matter tract connecting the superior frontal gyrus (SFG) to the inferior frontal gyrus (IFG). Its dorsal origin is identified in humans in the medial wall of the SFG, in the supplementary motor complex (SM-complex). However, empirical observation shows that many FAT fibres appear to originate from the dorsal, rather than medial, portion of the SFG. We quantitatively investigated the actual origin of FAT fibres in the SFG, specifically discriminating between terminations in the medial wall and in the convexity of the SFG. We analysed data from 105 subjects obtained from the Human Connectome Project (HCP) database. We parcelled the cortex of the IFG, dorsal SFG and medial SFG in several regions of interest (ROIs) ordered in a caudal-rostral direction, which served as seed locations for the generation of streamlines. Diffusion imaging data (DWI) was processed using a multi-shell multi-tissue CSD-based algorithm. Results showed that the number of streamlines originating from the dorsal wall of the SFG significantly exceeds those from the medial wall of the SFG. Connectivity patterns between ROIs indicated that FAT sub-bundles are segregated in parallel circuits ordered in a caudal-rostral direction. Such high degree of coherence in the streamline trajectory allows to establish pairs of homologous cortical parcels in the SFG and IFG. We conclude that the frontal origin of the FAT is found in both dorsal and medial surfaces of the superior frontal gyrus.

Brain morphological variability between whites and African Americans: the importance of racial identity in brain imaging research.

In a segregated society, marked by a historical background of inequalities, there is a consistent under-representation of ethnic and racial minorities in biomedical research, causing disparities in understanding genetic and acquired diseases as well as in the effectiveness of clinical treatments affecting different groups. The repeated inclusion of small and non-representative samples of the population in neuroimaging research has led to generalization bias in the morphological characterization of the human brain. A few brain morphometric studies between Whites and African Americans have reported differences in orbitofrontal volumetry and insula cortical thickness. Nevertheless, these studies are mostly conducted in small samples and populations with cognitive impairment. For this reason, this study aimed to identify brain morphological variability due to racial identity in representative samples. We hypothesized that, in neurotypical young adults, there are differences in brain morphometry between participants with distinct racial identities. We analyzed the Human Connectome Project (HCP) database to test this hypothesis. Brain volumetry, cortical thickness, and cortical surface area measures of participants identified as Whites (n = 338) or African Americans (n = 56) were analyzed. Non-parametrical permutation analysis of covariance between these racial identity groups adjusting for age, sex, education, and economic income was implemented. Results indicated volumetric differences in choroid plexus, supratentorial, white matter, and subcortical brain structures. Moreover, differences in cortical thickness and surface area in frontal, parietal, temporal, and occipital brain regions were identified between groups. In this regard, the inclusion of sub-representative minorities in neuroimaging research, such as African American persons, is fundamental for the comprehension of human brain morphometric diversity and to design personalized clinical brain treatments for this population.

Structural Connectivity of the Human Piriform Cortex: an Exploratory Study.

The piriform cortex (PC) is part of the primary olfactory network in humans. Recent findings suggest that it plays a role in pathophysiology of epilepsy. Therefore, studying its connectivity can further our understanding of seizure propagation in epilepsy. We aimed to explore the structural connectivity of PC using high-quality human connectome project data coupled with segmentation of PC on anatomic MRI. Twenty subjects were randomly selected from the human connectome project database, and PC was traced on each hemisphere. Probabilistic whole-brain tractography was then used to visualize PC connectivity. The strongest connectivity was noted between PC and ipsilateral insula in both hemispheres. Specifically, the posterior long gyrus of each insula was predominantly connected to PC. This was followed by connections between PC and basal ganglia as well as orbital frontal cortices. The PC has the strongest connectivity with the insula bilaterally. Specifically, the posterior long gyri of insula have the strongest connectivity. This finding may provide additional insight for localizing and treating temporo-insular epilepsy.

Are we really targeting and stimulating DLPFC by placing transcranial electrical stimulation (tES) electrodes over F3/F4?

In many clinical trials involving transcranial electrical stimulation (tES), target electrodes are typically placed over DLPFC with the assumption that this will primarily stimulate the underlying brain region. However, our study aimed to evaluate the electric fields (EF) that are actually delivered and identify prefrontal regions that may be inadvertently targeted in DLPFC tES. Head models were generated from the Human Connectome Project database's T1 + T2-weighted MRIs of 80 healthy adults. Two common DLPFC montages were simulated; symmetric-F4/F3, and asymmetric-F4/Fp1. Averaged EF was extracted from (1) the center of the target electrode (F4), and (2) the top 1% of voxels showing the strongest EF in individualized EF maps. Interindividual variabilities were quantified with the standard deviation of EF peak location/value. Similar steps were repeated with 66 participants with methamphetamine use disorder (MUDs) as an independent clinical population. In healthy adults, the group-level location of EF peaks was situated in the medial-frontopolar, and the individualized EF peaks were positioned in a cube with a volume of 29 cm3 /46 cm3 (symmetric/asymmetric montages). EFs in the frontopolar area were significantly higher than EF "under" the target electrode in both symmetric (peak: 0.41 ± 0.06, F4:0.22 ± 0.04) and asymmetric (peak: 0.38 ± 0.04, F4:0.2 ± 0.04) montages (Heges'g > 0.7). Similar results with slight between-group differences were found in MUDs. We highlighted that in common DLPFC tES montages, in addition to interindividual/intergroup variability, the frontopolar received the highest EFs rather than DLPFC as the main target. We specifically recommended considering the potential involvement of the frontopolar area as a mechanism underlying the effectiveness of DLPFC tES protocols.

Impact of sample size and regression of tissue-specific signals on effective connectivity within the core default mode network.

Interactions within brain networks are inherently directional, which are inaccessible to classical functional connectivity estimates from resting-state functional magnetic resonance imaging (fMRI) but can be detected using spectral dynamic causal modeling (DCM). The sample size and unavoidable presence of nuisance signals during fMRI measurement are the two important factors influencing the stability of group estimates of connectivity parameters. However, most recent studies exploring effective connectivity (EC) have been conducted with small sample sizes and minimally pre-processed datasets. We explore the impact of these two factors by analyzing clean resting-state fMRI data from 330 unrelated subjects from the Human Connectome Project database. We demonstrate that both the stability of the model selection procedures and the inference of connectivity parameters are highly dependent on the sample size. The minimum sample size required for stable DCM is approximately 50, which may explain the variability of the DCM results reported so far. We reveal a stable pattern of EC within the core default mode network computed for large sample sizes and demonstrate that the use of subject-specific thresholded whole-brain masks for tissue-specific signals regression enhances the detection of weak connections.

Disconnectome associated with progressive white matter hyperintensities in aging: a virtual lesion study.

White matter hyperintensities (WMH) are commonly seen on T2-weighted magnetic resonance imaging (MRI) in older adults and are associated with an increased risk of cognitive decline and dementia. This study aims to estimate changes in the structural connectome due to age-related WMH by using a virtual lesion approach. High-quality diffusion-weighted imaging data of 30 healthy subjects were obtained from the Human Connectome Project (HCP) database. Diffusion tractography using q-space diffeomorphic reconstruction (QSDR) and whole brain fiber tracking with 107 seed points was conducted using diffusion spectrum imaging studio and the brainnetome atlas was used to parcellate a total of 246 cortical and subcortical nodes. Previously published WMH frequency maps across age ranges (50's, 60's, 70's, and 80's) were used to generate virtual lesion masks for each decade at three lesion frequency thresholds, and these virtual lesion masks were applied as regions of avoidance (ROA) in fiber tracking to estimate connectivity changes. Connections showing significant differences in fiber density with and without ROA were identified using paired tests with False Discovery Rate (FDR) correction. Disconnections appeared first from the striatum to middle frontal gyrus (MFG) in the 50's, then from the thalamus to MFG in the 60's and extending to the superior frontal gyrus in the 70's, and ultimately including much more widespread cortical and hippocampal nodes in the 80's. Changes in the structural disconnectome due to age-related WMH can be estimated using the virtual lesion approach. The observed disconnections may contribute to the cognitive and sensorimotor deficits seen in aging.

The brainstem connections of the supplementary motor area and its relations to the corticospinal tract: Experimental rat and human 3-tesla tractography study

Although the supplementary motor area (SMA) is a large region on the medial surface of the frontal lobe of the brain, little is known about its function. The current study uses 3-tesla high-resolution diffusion tensor tractography (DTI) in healthy individuals and biotinylated dextran amine (BDA) and fluoro-gold (FG) tracer in rats to demonstrate the afferent and efferent connections of the SMA with brainstem structures. It also aims to clarify how SMA fibers relate to the corticospinal tract (CST). The BDA (n = 6) and FG (n = 8) tracers were pressure-injected into the SMA of 14 Wistar albino rats. Light and fluorescence microscopy was used to capture images of the FG and BDA-labeled cells and axons. High-resolution 3-tesla DTI data were acquired from the Human Connectome Project database. Tracts between the SMA and brainstem structures were analyzed using diffusion spectrum imaging (DSI) studio software. The FG injections into the SMA showed afferent projections from mesencephalic (periaqueductal gray matter, substantia nigra pars reticulata, ventral tegmental area, inferior colliculus, mesencephalic reticular, tegmental, and raphe nuclei), pontine (locus coeruleus, pontine reticular and vestibular nuclei), and medullary (area postrema, parabrachial, and medullary reticular nuclei) structures. The anterograde tracer BDA injections into the SMA showed efferent connections with mesencephalic (periaqueductal gray, substantia nigra pars compacta, dorsal raphe, trigeminal motor mesencephalic, and mesencephalic reticular nuclei), pontine (locus coeruleus, nucleus of the lateral lemniscus, vestibular, cochlear, and pontine reticular nuclei), and medullary (area postrema, medullary reticular, olivary, and parabrachial nuclei) structures. The SMA had efferent but no afferent connections with the cerebellar nuclei. The DTI results in healthy human subjects highly corresponded with the experimental results. Further, the DTI results showed a distinct bundle that descended to spinal levels closely related to the CST. Understanding SMA’s afferent and efferent connections will enrich our knowledge of its contribution to various brainstem networks and may provide new perspectives for understanding its motor and non-motor functions.

Spatiotemporal Developmental Gradient of Thalamic Morphology, Microstructure, and Connectivity fromthe Third Trimester to Early Infancy.

Thalamus is a critical component of the limbic system that is extensively involved in both basic and high-order brain functions. However, how the thalamic structure and function develops at macroscopic and microscopic scales during the perinatal period development is not yet well characterized. Here, we used multishell high-angular resolution diffusion MRI of 144 preterm-born and full-term infants in both sexes scanned at 32-44 postmenstrual weeks (PMWs) from the Developing Human Connectome Project database to investigate the thalamic development in morphology, microstructure, associated connectivity, and subnucleus division. We found evident anatomic expansion and linear increases of fiber integrity in the lateral side of thalamus compared with the medial part. The tractography results indicated that thalamic connection to the frontal cortex developed later than the other thalamocortical connections (parieto-occipital, motor, somatosensory, and temporal). Using a connectivity-based segmentation strategy, we revealed that functional partitions of thalamic subdivisions were formed at 32 PMWs or earlier, and the partition developed toward the adult pattern in a lateral-to-medial pattern. Collectively, these findings revealed faster development of the lateral thalamus than the central part as well as a posterior-to-anterior developmental gradient of thalamocortical connectivity from the third trimester to early infancy.SIGNIFICANCE STATEMENT This is the first study that characterizes the spatiotemporal developmental pattern of thalamus during the third trimester to early infancy. We found that thalamus develops in a lateral-to-medial pattern for both thalamic microstructures and subdivisions; and thalamocortical connectivity develops in a posterior-to-anterior gradient that thalamofrontal connectivity appears later than the other thalamocortical connections. These findings may enrich our understanding of the developmental principles of thalamus and provide references for the atypical brain growth in neurodevelopmental disorders.

Brain Connectivity: A Journal of Clinical Neurology, Neuroscience, & Neuroimaging Advancing the Field of Neurology

<i>Brain Connectivity: A Journal of Clinical Neurology, Neuroscience, &amp; Neuroimaging</i> Advancing the Field of Neurology

Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome.

Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.

Identification of central amygdala and trigeminal motor nucleus connectivity in humans: An ultra-high field diffusion MRI study.

The neuroanatomical circuitry of jaw muscles has been mostly explored in non-human animals. A recent rodent study revealed a novel circuit from the central amygdala (CeA) to the trigeminal motor nucleus (5M), which controls biting attacks. This circuit has yet to be delineated in humans. Ultra-high diffusion-weighted imaging data from the Human Connectome Project (HCP) allow in vivo delineation of circuits identified in other species-for example, the CeA-5M pathway-in humans. We hypothesized that the CeA-5M circuit could be resolved in humans at both 7 and 3 T. We performed probabilistic tractography between the CeA and 5M in 30 healthy young adults from the HCP database. As a negative control, we performed tractography between the basolateral amygdala (BLAT) and 5M, as CeA is the only amygdalar nucleus with extensive projections to the brainstem. Connectivity strength was operationalized as the number of streamlines between each region of interest. Connectivity strength between CeA-5M and BLAT-5M within each hemisphere was compared, and CeA-5M circuit had significantly stronger connectivity than the BLAT-5M circuit, bilaterally at both 7 T (all p < .001) and 3 T (all p < .001). This study is the first to delineate the CeA-5M circuit in humans.

P11.62.A The link between gliomas infiltrationandwhite matter architecture investigated with electron microscopy and diffusion tensor imaging

Abstract Background Diffuse low-grade gliomas (DLGG) display different preferential locations in eloquent and secondary associative brain areas. The reason for this tendency is still unknown. We hypothesized that the intrinsic architecture and water diffusion properties of the white matter bundles in these regions may facilitate gliomas infiltration. Material and Methods Magnetic resonance imaging of sixty-seven diffuse low-grade gliomas patients were normalized to/and segmented in MNI space to create three probabilistic infiltration weighted gradient maps according to the molecular status of each tumor group (IDH mutated, IDH wild-type and IDH mutated/1p19q co-deleted). Diffusion tensor imaging (DTI)- based parameters were derived for five major white matter bundles, displaying regional differences in the grade of infiltration, averaged over 20 healthy individuals acquired from the Human connectome project (HCP) database. Transmission electron microscopy (TEM) was used to analyze fiber density, fiber diameter and g-ratio in 100 human white matter regions, sampled from cadaver specimens, reflecting areas with different gliomas infiltration in each white matter bundle. Histological results and DTI-based parameters were compared in anatomical regions of high- and low grade of infiltration (HIF and LIF) respectively. Results We detected differences in the white matter infiltration of five major white matter bundles in three groups. IDHm infiltrated left fronto-temporal subcortical areas. IDHwt were detected in the posterior-temporal and temporo-parietal regions bilaterally. IDHm/1p19q infiltrated anterior subcortical regions of the frontal lobes bilaterally. Regional differences within the same white matter bundles were detected by both TEM- and DTI analysis linked to different topographical variables. Our multimodal analysis showed that HIF regions, common to all the groups, displayed a smaller fiber diameter, lower FA and higher RD compared with LIF regions. Conclusion Our results suggest that the both morphological features and diffusion parameters of the white matter may be different in regions linked to the preferential location of DLGG.

Normative mammillary body volumes: From the neonatal period to young adult

The mammillary bodies may be small, but they have an important role in encoding complex memories. Mammillary body pathology often occurs following thiamine deficiency but there is increasing evidence that the mammillary bodies are also compromised in other neurological conditions and in younger ages groups. For example, the mammillary bodies are frequently affected in neonates with hypoxic-ischemic encephalopathy. At present, there is no normative data for the mammillary bodies in younger groups making it difficult to identify abnormalities in neurological disorders. To address this, the present study set out to develop a normative dataset for neonates and for children to young adult. A further aim was to determine whether there were laterality or sex differences in mammillary body volumes. Mammillary body volumes were obtained from MRI scans from 506 participants across two datasets. Measures for neonates were acquired from the Developing Human Connectome Project database (156 male; 100 female); volumes for individuals aged 6–24 were acquired from the NICHE database (166 males; 84 females). Volume measurements were acquired using a semi-automated multi-atlas segmentation approach. Mammillary body volumes increased up to approximately 15 years-of-age. The left mammillary body was marginally, but significantly, larger than the right in the neonates with a similar pattern in older children/young adults. In neonates, the mammillary bodies in males were slightly bigger than females but no sex differences were present in older children/young adults. Given the increasing presentation of mammillary body pathology in neonates and children, these normative data will enable better assessment of the mammillary bodies in healthy and at-risk populations.

Deep learning models of cognitive processes constrained by human brain connectomes

Decoding cognitive processes from recordings of brain activity has been an active topic in neuroscience research for decades. Traditional decoding studies focused on pattern classification in specific regions of interest and averaging brain activity over many trials. Recently, brain decoding with graph neural networks has been shown to scale at fine temporal resolution and on the full brain, achieving state-of-the-art performance on the human connectome project benchmark. The reason behind this success is likely the strong inductive connectome prior that enables the integration of distributed patterns of brain activity. Yet, the nature of such inductive bias is still poorly understood. In this work, we investigate the impact of the inclusion of multiple path lengths (through high-order graph convolution), the homogeneity of brain parcels (graph nodes), and the type of interactions (graph edges). We evaluate the decoding models on a large population of 1200 participants, under 21 different experimental conditions, acquired from the Human Connectome Project database.Our findings reveal that the optimal choice for large-scale cognitive decoding is to propagate neural dynamics within empirical functional connectomes and integrate brain dynamics using high-order graph convolutions. In this setting, the model exhibits high decoding accuracy and robustness against adversarial attacks on the graph architecture, including randomization in functional connectomes and lesions in targeted brain regions and networks. The trained model relies on biologically meaningful features for the prediction of cognitive states and generates task-specific graph representations resembling task-evoked activation maps. These results demonstrate that a full-brain integrative model is critical for the large-scale brain decoding. Our study establishes principles of how to effectively leverage human connectome constraints in deep graph neural networks, providing new avenues to study the neural substrates of human cognition at scale.

Multimodal and multicontrast image fusion via deep generative models

Recently, it has become progressively more evident that classic diagnostic labels are unable to accurately and reliably describe the complexity and variability of several clinical phenotypes. This is particularly true for a broad range of neuropsychiatric illnesses such as depression and anxiety disorders or behavioural phenotypes such as aggression and antisocial personality. Patient heterogeneity can be better described and conceptualized by grouping individuals into novel categories, which are based on empirically-derived sections of intersecting continua that span both across and beyond traditional categorical borders. In this context, neuroimaging data (i.e. the set of images which result from functional/metabolic (e.g. functional magnetic resonance imaging, functional near-infrared spectroscopy, or positron emission tomography) and structural (e.g. computed tomography, T1-, T2- PD- or diffusion weighted magnetic resonance imaging) carry a wealth of spatiotemporally resolved information about each patient's brain. However, they are usually heavily collapsed a priori through procedures which are not learned as part of model training, and consequently not optimized for the downstream prediction task. This is due to the fact that every individual participant usually comes with multiple whole-brain 3D imaging modalities often accompanied by a deep genotypic and phenotypic characterization, hence posing formidable computational challenges.In this paper we design and validate a deep learning architecture based on generative models rooted in a modular approach and separable convolutional blocks (which result in a 20-fold decrease in parameter utilization) in order to a) fuse multiple 3D neuroimaging modalities on a voxel-wise level, b) efficiently convert them into informative latent embeddings through heavy dimensionality reduction, c) maintain good generalizability and minimal information loss. As proof of concept, we test our architecture on the well characterized Human Connectome Project database (n = 974 healthy subjects), demonstrating that our latent embeddings can be clustered into easily separable subject strata which, in turn, map to different phenotypical information (including organic, neuropsychological, personality variables) which was not included in the embedding creation process.The ability to extract meaningful and separable phenotypic information from brain images alone can aid in creating multi-dimensional biomarkers able to chart spatio-temporal trajectories which may correspond to different pathophysiological mechanisms unidentifiable to traditional data analysis approaches. In turn, this may be of aid in predicting disease evolution as well as drug response, hence supporting mechanistic disease understanding and also empowering clinical trials.