Fetal development of functional thalamocortical and cortico-cortical connectivity.

Measuring and understanding functional fetal brain development in utero is critical for the study of the developmental foundations of our cognitive abilities, possible early detection of disorders, and their prevention. Thalamocortical connections are an intricate component of shaping the cortical layout, but so far, only ex-vivo studies provide evidence of how axons enter the sub-plate and cortex during this highly dynamic phase. Evidence for normal in-utero development of the functional thalamocortical connectome in humans is missing. Here, we modeled fetal functional thalamocortical connectome development using in-utero functional magnetic resonance imaging in fetuses observed from 19th to 40th weeks of gestation (GW). We observed a peak increase of thalamocortical functional connectivity strength between 29th and 31st GW, right before axons establish synapses in the cortex. The cortico-cortical connectivity increases in a similar time window, and exhibits significant functional laterality in temporal-superior, -medial, and -inferior areas. Homologous regions exhibit overall similar mirrored connectivity profiles, but this similarity decreases during gestation giving way to a more diverse cortical interconnectedness. Our results complement the understanding of structural development of the human connectome and may serve as the basis for the investigation of disease and deviations from a normal developmental trajectory of connectivity development.

Omnipresence of the sensorimotor-association axis topography in the human connectome.

Low-dimensional representations are increasingly used to study meaningful organizational principles within the human brain. Most notably, the sensorimotor-association axis consistently explains the most variance in the human connectome as its so-called principal gradient, suggesting that it represents a fundamental organizational principle. While recent work indicates these low dimensional representations are relatively robust, they are limited by modeling only certain aspects of the functional connectivity structure. To date, the majority of studies have restricted these approaches to the strongest connections in the brain, treating weaker or negative connections as noise despite evidence of meaningful structure among them. The present work examines connectivity gradients of the human connectome across a full range of connectivity strengths and explores the implications for outcomes of individual differences, identifying potential dependencies on thresholds and opportunities to improve prediction tasks. Interestingly, the sensorimotor-association axis emerged as the principal gradient of the human connectome across the entire range of connectivity levels. Moreover, the principal gradient of connections at intermediate strengths encoded individual differences, better followed individual-specific anatomical features, and was also more predictive of intelligence. Taken together, our results add to evidence of the sensorimotor-association axis as a fundamental principle of the brain's functional organization, since it is evident even in the connectivity structure of more lenient connectivity thresholds. These more loosely coupled connections further appear to contain valuable and potentially important information that could be used to improve our understanding of individual differences, diagnosis, and the prediction of treatment outcomes.

Visual outcomes after anterior temporal lobectomy and transsylvian selective amygdalohippocampectomy: A quantitative comparison of clinical and diffusion data.

OBJECTIVE: Anterior temporal lobectomy (ATL) and transsylvian selective amygdalohippocampectomy (tsSAHE) are effective treatment strategies for intractable temporal lobe epilepsy but may cause visual field deficits (VFDs) by damaging the optic radiation (OpR). Due to the OpR's considerable variability and because it is indistinguishable from surrounding tissue without further technical guidance, it is highly vulnerable to iatrogenic injury. This imaging study uses a multimodal approach to assess visual outcomes after epilepsy surgery. METHODS: We studied 62 patients who underwent ATL (n = 32) or tsSAHE (n = 30). Analysis of visual outcomes was conducted in four steps, including the assessment of (1) perimetry outcome (VFD incidence/extent, n = 44/40), (2) volumetric OpR tractography damage (n = 55), and the (3) relation of volumetric OpR tractography damage and perimetry outcome (n = 35). Furthermore, (4) fixel-based analysis (FBA) was performed to assess micro- and macrostructural changes within the OpR following surgery (n = 36). RESULTS: Altogether, 56% of all patients had postoperative VFDs (78.9% after ATL, 36.36% after tsSAHE, p = .011). VFDs and OpR tractography damage tended to be more severe within the ATL group (ATL vs. tsSAHE, integrity of contralateral upper quadrant: 65% vs. 97%, p = .002; OpR tractography damage: 69.2 mm3 vs. 3.8 mm3 , p = .002). Volumetric OpR tractography damage could reliably predict VFD incidence (86% sensitivity, 78% specificity) and could significantly explain VFD extent (R2  = .47, p = .0001). FBA revealed a more widespread decline of fibre cross-section within the ATL group. SIGNIFICANCE: In the context of controversial visual outcomes following epilepsy surgery, this study provides clinical as well as neuroimaging evidence for a higher risk and greater severity of postoperative VFDs after ATL compared to tsSAHE. Volumetric OpR tractography damage is a feasible parameter to reliably predict this morbidity in both treatment groups and may ultimately support personalized planning of surgical candidates. Advanced diffusion analysis tools such as FBA offer a structural explanation of surgically induced visual pathway damage, allowing noninvasive quantification and visualization of micro- and macrostructural tract affection.

Unsupervised machine learning identifies predictive progression markers of IPF.

OBJECTIVES: To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome. METHODS: We studied radiological disease progression in 76 patients with IPF, including overall 190 computed tomography (CT) examinations of the chest. An algorithm identified candidates for imaging patterns marking progression by computationally clustering visual CT features. A classification algorithm selected clusters associated with radiological disease progression by testing their value for recognizing the temporal sequence of examinations. This resulted in radiological disease progression signatures, and pathways of lung tissue change accompanying progression observed across the cohort. Finally, we tested if the dynamics of marker patterns predict outcome, and performed an external validation study on a cohort from a different center. RESULTS: Progression marker patterns were identified and exhibited high stability in a repeatability experiment with 20 random sub-cohorts of the overall cohort. The 4 top-ranked progression markers were consistently selected as most informative for progression across all random sub-cohorts. After spatial image registration, local tracking of lung pattern transitions revealed a network of tissue transition pathways from healthy to a sequence of disease tissues. The progression markers were predictive for outcome, and the model achieved comparable results on a replication cohort. CONCLUSIONS: Unsupervised learning can identify radiological disease progression markers that predict outcome. Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. KEY POINTS: • Unsupervised learning can identify radiological disease progression markers that predict outcome in patients with idiopathic pulmonary fibrosis. • Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. • The progression markers achieved comparable results on a replication cohort.

Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data.

Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement and maternal breathing and consequently to suppress erroneous signal correlations. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint with least motion artefacts as reference volume, and perform interpolation for the reconstruction of the motion corrected time series. The results can suffer, if no low-motion frame is available, and if reconstruction does not exploit any assumptions about the continuity of the fMRI signal. Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI. We performed an extensive parameter study to investigate the effectiveness of motion estimation and present in this work benchmark metrics to quantify the effect of motion correction and regularised volumetric reconstruction approaches on functional connectivity computations. We demonstrate the proposed framework's ability to improve functional connectivity estimates, reproducibility and signal interpretability, which is clinically highly desirable for the establishment of prognostic noninvasive imaging biomarkers. The motion correction and volumetric reconstruction framework is made available as an open-source package of NiftyMIC.

Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment.

The human brain varies across individuals in its morphology, function, and cognitive capacities. Variability is particularly high in phylogenetically modern regions associated with higher order cognitive abilities, but its relationship to the layout and strength of functional networks is poorly understood. In this study we disentangled the variability of two key aspects of functional connectivity: strength and topography. We then compared the genetic and environmental influences on these two features. Genetic contribution is heterogeneously distributed across the cortex and differs for strength and topography. In heteromodal areas genes predominantly affect the topography of networks, while their connectivity strength is shaped primarily by random environmental influence such as learning. We identified peak areas of genetic control of topography overlapping with parts of the processing stream from primary areas to network hubs in the default mode network, suggesting the coordination of spatial configurations across those processing pathways. These findings provide a detailed map of the diverse contribution of heritability and individual experience to the strength and topography of functional brain architecture.

The Prenatal Morphomechanic Impact of Agenesis of the Corpus Callosum on Human Brain Structure and Asymmetry.

Genetic, molecular, and physical forces together impact brain morphogenesis. The early impact of deficient midline crossing in agenesis of the Corpus Callosum (ACC) on prenatal human brain development and architecture is widely unknown. Here we analyze the changes of brain structure in 46 fetuses with ACC in vivo to identify their deviations from normal development. Cases of complete ACC show an increase in the thickness of the cerebral wall in the frontomedial regions and a reduction in the temporal, insular, medial occipital and lateral parietal regions, already present at midgestation. ACC is associated with a more symmetric configuration of the temporal lobes and increased frequency of atypical asymmetry patterns, indicating an early morphomechanic effect of callosal growth on human brain development affecting the thickness of the pallium along a ventro-dorsal gradient. Altered prenatal brain architecture in ACC emphasizes the importance of conformational forces introduced by emerging interhemispheric connectivity on the establishment of polygenically determined brain asymmetries.

Cross-species functional alignment reveals evolutionary hierarchy within the connectome.

Evolution provides an important window into how cortical organization shapes function and vice versa. The complex mosaic of changes in brain morphology and functional organization that have shaped the mammalian cortex during evolution, complicates attempts to chart cortical differences across species. It limits our ability to fully appreciate how evolution has shaped our brain, especially in systems associated with unique human cognitive capabilities that lack anatomical homologues in other species. Here, we develop a function-based method for cross-species alignment that enables the quantification of homologous regions between humans and rhesus macaques, even when their location is decoupled from anatomical landmarks. Critically, we find cross-species similarity in functional organization reflects a gradient of evolutionary change that decreases from unimodal systems and culminates with the most pronounced changes in posterior regions of the default mode network (angular gyrus, posterior cingulate and middle temporal cortices). Our findings suggest that the establishment of the default mode network, as the apex of a cognitive hierarchy, has changed in a complex manner during human evolution - even within subnetworks.

Joint embedding: A scalable alignment to compare individuals in a connectivity space.

A common coordinate space enabling comparison across individuals is vital to understanding human brain organization and individual differences. By leveraging dimensionality reduction algorithms, high-dimensional fMRI data can be represented in a low-dimensional space to characterize individual features. Such a representative space encodes the functional architecture of individuals and enables the observation of functional changes across time. However, determining comparable functional features across individuals in resting-state fMRI in a way that simultaneously preserves individual-specific connectivity structure can be challenging. In this work we propose scalable joint embedding to simultaneously embed multiple individual brain connectomes within a common space that allows individual representations across datasets to be aligned. Using Human Connectome Project data, we evaluated the joint embedding approach by comparing it to the previously established orthonormal alignment model. Alignment using joint embedding substantially increased the similarity of functional representations across individuals while simultaneously capturing their distinct profiles, allowing individuals to be more discriminable from each other. Additionally, we demonstrated that the common space established using resting-state fMRI provides a better overlap of task-activation across participants. Finally, in a more challenging scenario - alignment across a lifespan cohort aged from 6 to 85 - joint embedding provided a better prediction of age (r2 = 0.65) than the prior alignment model. It facilitated the characterization of functional trajectories across lifespan. Overall, these analyses establish that joint embedding can simultaneously capture individual neural representations in a common connectivity space aligning functional data across participants and populations and preserve individual specificity.

Diffeomorphic functional brain surface alignment: Functional demons.

Aligning brain structures across individuals is a central prerequisite for comparative neuroimaging studies. Typically, registration approaches assume a strong association between the features used for alignment, such as macro-anatomy, and the variable observed, such as functional activation or connectivity. Here, we propose to use the structure of intrinsic resting state fMRI signal correlation patterns as a basis for alignment of the cortex in functional studies. Rather than assuming the spatial correspondence of functional structures between subjects, we have identified locations with similar connectivity profiles across subjects. We mapped functional connectivity relationships within the brain into an embedding space, and aligned the resulting maps of multiple subjects. We then performed a diffeomorphic alignment of the cortical surfaces, driven by the corresponding features in the joint embedding space. Results show that functional alignment based on resting state fMRI identifies functionally homologous regions across individuals with higher accuracy than alignment based on the spatial correspondence of anatomy. Further, functional alignment enables measurement of the strength of the anatomo-functional link across the cortex, and reveals the uneven distribution of this link. Stronger anatomo-functional dissociation was found in higher association areas compared to primary sensory- and motor areas. Functional alignment based on resting state features improves group analysis of task based functional MRI data, increasing statistical power and improving the delineation of task-specific core regions. Finally, a comparison of the anatomo-functional dissociation between cohorts is demonstrated with a group of left and right handed subjects.

Fast connectivity gradient approximation: maintaining spatially fine-grained connectivity gradients while reducing computational costs.

Brain connectome analysis suffers from the high dimensionality of connectivity data, often forcing a reduced representation of the brain at a lower spatial resolution or parcellation. This is particularly true for graph-based representations, which are increasingly used to characterize connectivity gradients, capturing patterns of systematic spatial variation in the functional connectivity structure. However, maintaining a high spatial resolution is crucial for enabling fine-grained topographical analysis and preserving subtle individual differences that might otherwise be lost. Here we introduce a computationally efficient approach to establish spatially fine-grained connectivity gradients. At its core, it leverages a set of landmarks to approximate the underlying connectivity structure at the full spatial resolution without requiring a full-scale vertex-by-vertex connectivity matrix. We show that this approach reduces computational time and memory usage while preserving informative individual features and demonstrate its application in improving brain-behavior predictions. Overall, its efficiency can remove computational barriers and enable the widespread application of connectivity gradients to capture spatial signatures of the connectome. Importantly, maintaining a spatially fine-grained resolution facilitates to characterize the spatial transitions inherent in the core concept of gradients of brain organization.

Evolution of cortical geometry and its link to function, behaviour and ecology.

Studies in comparative neuroanatomy and of the fossil record demonstrate the influence of socio-ecological niches on the morphology of the cerebral cortex, but have led to oftentimes conflicting theories about its evolution. Here, we study the relationship between the shape of the cerebral cortex and the topography of its function. We establish a joint geometric representation of the cerebral cortices of ninety species of extant Euarchontoglires, including commonly used experimental model organisms. We show that variability in surface geometry relates to species' ecology and behaviour, independent of overall brain size. Notably, ancestral shape reconstruction of the cortical surface and its change during evolution enables us to trace the evolutionary history of localised cortical expansions, modal segregation of brain function, and their association to behaviour and cognition. We find that individual cortical regions follow different sequences of area increase during evolutionary adaptations to dynamic socio-ecological niches. Anatomical correlates of this sequence of events are still observable in extant species, and relate to their current behaviour and ecology. We decompose the deep evolutionary history of the shape of the human cortical surface into spatially and temporally conscribed components with highly interpretable functional associations, highlighting the importance of considering the evolutionary history of cortical regions when studying their anatomy and function.

Cortical thickness in the right medial frontal gyrus predicts planning performance in healthy children and adolescents.

The ability to plan is an important part of the set of the cognitive skills called "executive functions." To be able to plan actions in advance is of great importance in everyday life and constitutes one of the major key features for academic as well as economic success. The present study aimed to investigate the neuroanatomical correlates of planning in normally developing children, as measured by the cortical thickness of the prefrontal cortex. Eighteen healthy children and adolescents underwent structural MRI examinations and the Tower of London (ToL) task. A multiple regression analysis revealed that the cortical thickness of the right caudal middle frontal gyrus (cMFG) was a significant predictor of planning performance. Neither the cortical thickness of any other prefrontal area nor gender were significantly associated with performance in the ToL task. The results of the present exploratory study suggest that the cortical thickness of the right, but not the left cMFG, is positively correlated with performance in the ToL task. We, therefore, conclude that increased cortical thickness may be more beneficial for higher-order processes, such as information integration, than for lower-order processes, such as the analysis of external information.

Radiomic features define risk and are linked to DNA methylation attributes in primary CNS lymphoma.

Background: The prognostic roles of clinical and laboratory markers have been exploited to model risk in patients with primary CNS lymphoma, but these approaches do not fully explain the observed variation in outcome. To date, neuroimaging or molecular information is not used. The aim of this study was to determine the utility of radiomic features to capture clinically relevant phenotypes, and to link those to molecular profiles for enhanced risk stratification. Methods: In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005-2018) and an external validation site in South Korea (44 patients, 2013-2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines. Results: The radiomic risk score, consisting of 20 mostly textural features, was a strong and independent predictor of survival (multivariate hazard ratio = 6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients. Conclusions: The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.

Machine learning in neuroimaging: from research to clinical practice.

Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.

FLAIR2 post-processing: improving MS lesion detection in standard MS imaging protocols.

BACKGROUND: Technical improvements in magnetic resonance imaging (MRI) acquisition, such as higher field strength and optimized sequences, lead to better multiple sclerosis (MS) lesion detection and characterization. Multiplication of 3D-FLAIR with 3D-T2 sequences (FLAIR2) results in isovoxel images with increased contrast-to-noise ratio, increased white-gray-matter contrast, and improved MS lesion visualization without increasing MRI acquisition time. The current study aims to assess the potential of 3D-FLAIR2 in detecting cortical/leucocortical (LC), juxtacortical (JC), and white matter (WM) lesions. OBJECTIVE: To compare lesion detection of 3D-FLAIR2 with state-of-the-art 3D-T2-FLAIR and 3D-T2-weighted images. METHODS: We retrospectively analyzed MRI scans of thirteen MS patients, showing previously noted high cortical lesion load. Scans were acquired using a 3 T MRI scanner. WM, JC, and LC lesions were manually labeled and manually counted after randomization of 3D-T2, 3D-FLAIR, and 3D-FLAIR2 scans using the ITK-SNAP tool. RESULTS: LC lesion visibility was significantly improved by 3D-FLAIR2 in comparison to 3D-FLAIR (4 vs 1; p = 0.018) and 3D-T2 (4 vs 1; p = 0.007). Comparing LC lesion detection in 3D-FLAIR2 vs. 3D-FLAIR, 3D-FLAIR2 detected on average 3.2 more cortical lesions (95% CI - 9.1 to 2.8). Comparing against 3D-T2, 3D-FLAIR2 detected on average 3.7 more LC lesions (95% CI 3.3-10.7). CONCLUSIONS: 3D-FLAIR2 is an easily applicable time-sparing MR post-processing method to improve cortical lesion detection. Larger sampled studies are warranted to validate the sensitivity and specificity of 3D-FLAIR2.

Imaging visuospatial memory in temporal lobe epilepsy-Results of an fMRI study.

PURPOSE: Impairment of cognitive functions is commonly observed in temporal lobe epilepsy (TLE). The aim of this study was to assess visuospatial memory functions and memory-related networks using an adapted version of Roland's Hometown Walking (RHWT) functional MRI (fMRI) task in patients with TLE. METHODS: We used fMRI to study activation patterns based on a visuospatial memory paradigm in 32 TLE patients (9 right; 23 left) and also within subgroups of lesional and non-lesional TLE. To test for performance, a correlational analysis of fMRI activation patterns and out-of-scanner neuropsychological visuospatial memory testing was performed. Additionally, we assessed memory-related networks using functional connectivity (FC). RESULTS: Greater contralateral than ipsilateral mesiotemporal (parahippocampal gyrus/hippocampus) activation was observed in left (n = 23)/right (n = 9) TLE. In lesional left TLE (n = 17), significant activations were seen in right more than left mesiotemporal areas (parahippocampal gyrus), while non-lesional left TLE patients (n = 6) showed significant bilateral (left>right) activations in mesiotemporal structures (parahippocampal gyrus). In left TLE, visuospatial cognitive testing correlated with fMRI activations in left (parahippocampal gyrus) and right mesiotemporal structures (hippocampus), characterized by greater fMRI activation being associated with better memory scores. In right TLE, higher scores in visuospatial memory testing were associated with greater fMRI activations in left and right insular regions. FC patterns of memory-related networks differ in right and left TLE. CONCLUSION: While TLE in general leads to asymmetrical mesiotemporal activation, lesion-induced and non-lesional TLE patients reveal different memory fMRI activation patterns. In right TLE, insular regions try to compensate for impaired right mesiotemporal structures during the performance of visuospatial tasks. Underlying functional visuospatial memory networks differ in right and left TLE.

Connectome Analysis in an Individual with SETD1B -Related Neurodevelopmental Disorder and Epilepsy.

OBJECTIVE: Causative variants in SETD1B , encoding a lysine-specific methyltransferase, have recently been associated with a neurodevelopmental phenotype encompassing intellectual disability, autistic features, pronounced language delay, and epilepsy. It has been noted that long-term and deep phenotype data are needed to further delineate this rare condition. METHODS: In this study, we provide an in-depth clinical characterization with long-term follow-up and trio exome sequencing findings to describe one additional individual affected by SETD1B -related disorder. The diagnostic workup was complemented by a functional magnetic resonance imaging (fMRI) study. RESULTS: We report a 24-year-old male individual with an early-onset neurodevelopmental disorder with epilepsy due to the de novo missense variant c.5699A>G, p.(Tyr1900Cys) in SETD1B (NM_015048.1). He exhibited delayed speech development, autism spectrum disorder, and early-onset epilepsy with absence and generalized tonic-clonic seizures. Despite profoundly impaired communication skills, ongoing improvements regarding language production have been noted in adulthood. fMRI findings demonstrate abnormal language activation and resting-state connectivity structure. CONCLUSION: Our report expands the previously delineated phenotype of SETD1B -related disorder and provides novel insights into underlying disease mechanisms.

White matter integrity is disrupted in adolescents with acute anorexia nervosa: A diffusion tensor imaging study.

Anorexia nervosa (AN) is a highly debilitating mental illness with multifactorial etiology. It oftentimes begins in adolescence, therefore understanding the pathophysiology in this period is important. Few studies investigated the possible impact of the acute state of illness on white matter (WM) tissue properties in the developing adolescent brain. The present study expands our understanding of the implications of AN and starvation on WM integrity. 67 acutely ill adolescent patients suffering from AN restricting type were compared with 32 healthy controls using diffusion tensor imaging assessing fractional anisotropy (FA) and mean diffusivity (MD). We found widespread alterations in the vast majority of the WM regions with significantly decreased FA and increased MD in the AN group. In this highly selective sample in the acute stage of AN, the alterations are likely to be the consequence of starvation. Still, we cannot rule out that some of the affected regions might play a key role in AN-specific psychopathology.

The impact of hippocampal impairment on task-positive and task-negative language networks in temporal lobe epilepsy.

OBJECTIVE: To study hippocampal integration within task-positive and task-negative language networks and the impact of a diseased left and right hippocampus on the language connectome in temporal lobe epilepsy (TLE). METHODS: We used functional magnetic resonance imaging (fMRI) to study a homogenous group of 32 patients with TLE (17 left) and 14 healthy controls during a verb-generation task. We performed functional connectivity analysis and quantified alterations within the language connectome and evaluated disruptions of the functional dissociation along the anterior-posterior axis of the hippocampi. RESULTS: Connectivity analysis revealed significant differences between left and right TLE compared to healthy controls. Left TLE showed widespread impairment of task-positive language networks, while right TLE showed less pronounced alterations. Particularly right TLE showed altered connectivity for cortical regions that were part of the default mode network (DMN). Left TLE showed a disturbed functional dissociation pattern along the left hippocampus to left and right inferior frontal language regions, while left and right TLE revealed an altered dissociation pattern along the right hippocampus to regions associated with the DMN. CONCLUSIONS: Our results showed an impaired hippocampal integration into active language and the default mode networks, which both may contribute to language impairment in TLE. SIGNIFICANCE: Our results emphasize the direct role of the left hippocampus in language processing, and the potential role of the right hippocampus as a modulator between DMN and task-positive networks.