Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox

Structural brain network in relation to language in school-aged extremely preterm children: A diffusion tensor imaging study

Between 22 and 45 % of children born preterm experience difficulties with expressive and receptive language when they reach school age. Little is currently known about the neural mechanisms behind their linguistic performance. This study investigates the brain areas and white matter connections that form the structural language network in extremely preterm-born children who have reached school age. Structural brain connectivity was quantified using diffusion-weighted imaging (DWI) and tractography in n = 58 (62 % female) extremely preterm-born children aged 8-12 years. Language outcomes were assessed using the CELF-4-NL Recalling Sentences subtest. Language scores were below average in n = 13 (22 %) children. Language outcomes related significantly to a subnetwork of 16 brain regions (p = 0.012). The network comprised brain regions from the left hemisphere including the pars orbitalis, middle and superior frontal gyrus, frontal pole, pre- and postcentral gyrus, superior temporal gyrus, insula, caudate nucleus, thalamus, and putamen. In the right hemisphere, the anterior cingulate was part of the network. These findings suggest that extremely preterm children rely mostly on their left hemisphere during language processing, which is similar to typically developing children. However, they seem to use compensatory neural pathways that include brain areas right next to the areas typically involved in language processing. These areas include the pars orbitalis (adjacent to Broca's area) and the putamen and caudate nucleus (adjacent to the limbic system). It is important to note that language difficulties were not necessarily related to brain injury around birth.

Insomnia Subtypes Have Differentiating Deviations in Brain Structural Connectivity

BACKGROUND

Insomnia disorder is the most common sleep disorder. A better understanding of insomnia-related deviations in the brain could inspire better treatment. Insufficiently recognized heterogeneity within the insomnia population could obscure detection of involved brain circuits. In the current study, we investigated whether structural brain connectivity deviations differed between recently discovered and validated insomnia subtypes.

METHODS

Structural and diffusion-weighted 3T magnetic resonance imaging data from 4 independent studies were harmonized. The sample consisted of 73 control participants without sleep complaints and 204 participants with insomnia who were grouped into 5 insomnia subtypes based on their fingerprint of mood and personality traits assessed with the Insomnia Type Questionnaire. Linear regression correcting for age and sex was used to evaluate group differences in structural connectivity strength, indicated by fractional anisotropy, streamline volume density, and mean diffusivity and evaluated within 3 different atlases.

RESULTS

Insomnia subtypes showed differentiating profiles of deviating structural connectivity that were concentrated in different functional networks. Permutation testing against randomly drawn heterogeneous subsamples indicated significant specificity of deviation profiles in 4 of the 5 subtypes: highly distressed, moderately distressed reward sensitive, slightly distressed low reactive, and slightly distressed high reactive. Connectivity deviation profile significance ranged from p = .001 to p = .049 for different resolutions of brain parcellation and connectivity weight.

CONCLUSIONS

Our results provide an initial indication that different insomnia subtypes exhibit distinct profiles of deviations in structural brain connectivity. Subtyping insomnia may be essential for a better understanding of brain mechanisms that contribute to insomnia vulnerability.

Structural-functional connectomics in major depressive disorder following aiTBS treatment

Major depressive disorder (MDD) has been associated with changes in the structural (SC) and functional connectivity (FC) of the brain. This study investigated the effects of accelerated intermittent theta burst stimulation (aiTBS) on SC-FC coupling and graph theory measures, focusing on the association between baseline SC-FC coupling of the dorsolateral prefrontal cortex (dlPFC) and clinical improvement. In a randomized, sham-controlled, quadruple-blind, crossover study, aiTBS was delivered to the left dlPFC of depressed patients with MDD, and diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rsfMRI) data were acquired. In 77 MDD patients, significantly increased whole-brain SC-FC coupling was observed, primarily driven by default mode network (DMN) SC-FC coupling, along with increased somatomotor network FC, and decreased FC between the DMN hubs and limbic regions after active aiTBS. Furthermore, significant increases were observed in structural global and local efficiency measures that were not specific to the stimulation condition (active/sham aiTBS). However, these changes did not significantly correlate with clinical improvement. Notably, baseline SC-FC coupling of the left dlPFC was a significant predictor of clinical improvement. Our findings highlight the potential of left dlPFC SC-FC coupling as a predictor of aiTBS treatment outcomes, as well as the effect of aiTBS in enhancing SC-FC coupling.

Polyconnectomic Scoring of Functional Connectivity Patterns Across Eight Neuropsychiatric and Three Neurodegenerative Disorders

BACKGROUND

Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches that search for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.

METHODS

The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity mirrors the whole-brain circuitry characteristics of a trait. We computed PCSs for 8 neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and 3 neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional magnetic resonance imaging data from 10,667 individuals (5325 patients, 5342 control participants). We also examined PCSs in 26,673 individuals from the population-based UK Biobank cohort.

RESULTS

PCSs were consistently higher in out-of-sample patients across 6 of the 8 neuropsychiatric and across all 3 investigated neurodegenerative disorders ([minimum, maximum]: area under the receiver operating characteristic curve = [0.55, 0.73], false discovery rate-corrected p [pFDR] = [1.8 × 10-16, 4.5 × 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 × 10-5), lower cognitive performance (pFDR < 5.3 × 10-5), and lower general well-being (pFDR < 9.7 × 10-4).

CONCLUSIONS

Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. Polyconnectomic scoring effectively aggregates disorder-related signatures across the entire brain into an interpretable, participant-specific metric. A toolbox is provided for PCS computation.

The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression

Reduced processing speed is a core deficit in major depressive disorder (MDD) and has been linked to altered structural brain network connectivity. Ample evidence highlights the involvement of genetic-immunological processes in MDD and specific depressive symptoms. Here, we extended these findings by examining associations between polygenic scores for tumor necrosis factor-α blood levels (TNF-α PGS), structural brain connectivity, and processing speed in a large sample of MDD patients. Processing speed performance of n = 284 acutely depressed, n = 177 partially and n = 198 fully remitted patients, and n = 743 healthy controls (HC) was estimated based on five neuropsychological tests. Network-based statistic was used to identify a brain network associated with processing speed. We employed general linear models to examine the association between TNF-α PGS and processing speed. We investigated whether network connectivity mediates the association between TNF-α PGS and processing speed. We identified a structural network positively associated with processing speed in the whole sample. We observed a significant negative association between TNF-α PGS and processing speed in acutely depressed patients, whereas no association was found in remitted patients and HC. The mediation analysis revealed that brain connectivity partially mediated the association between TNF-α PGS and processing speed in acute MDD. The present study provides evidence that TNF-α PGS is associated with decreased processing speed exclusively in patients with acute depression. This association was partially mediated by structural brain connectivity. Using multimodal data, the current findings advance our understanding of cognitive dysfunction in MDD and highlight the involvement of genetic-immunological processes in its pathomechanisms.

Age Trajectories of the Structural Connectome in Child and Adolescent Offspring of Individuals With Bipolar Disorder or Schizophrenia

Background

Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated.

Methods

A total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity.

Results

Offspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder.

Conclusions

The findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.

Transcriptomic and neuroimaging data integration enhances machine learning classification of schizophrenia

Background

Schizophrenia is a polygenic disorder associated with changes in brain structure and function. Integrating macroscale brain features with microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers for schizophrenia.

Objective

We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models.

Methods

We collected brain imaging data and blood RNA sequencing data from 43 patients with schizophrenia and 60 age- and gender-matched healthy controls, and we extracted multi-omics features of macroscale brain morphology, brain structural and functional connectivity, and gene transcription of schizophrenia risk genes. Multi-scale data fusion was performed using a machine learning integration framework, together with several conventional machine learning methods and neural networks for patient classification.

Results

We found that multi-omics data fusion in conventional machine learning models achieved the highest accuracy (AUC ~0.76-0.92) in contrast to the single-modality models, with AUC improvements of 8.88 to 22.64%. Similar findings were observed for the neural network, showing an increase of 16.57% for the multimodal classification model (accuracy 71.43%) compared to the single-modal average. In addition, we identified several brain regions in the left posterior cingulate and right frontal pole that made a major contribution to disease classification.

Conclusion

We provide empirical evidence for the increased accuracy achieved by imaging genetic data integration in schizophrenia classification. Multi-scale data fusion holds promise for enhancing diagnostic precision, facilitating early detection and personalizing treatment regimens in schizophrenia.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

The significance of structural rich club hubs for the processing of hierarchical stimuli

The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.

Shared and distinct structural brain networks related to childhood maltreatment and social support: connectome-based predictive modeling

Childhood maltreatment (CM) has been associated with changes in structural brain connectivity even in the absence of mental illness. Social support, an important protective factor in the presence of childhood maltreatment, has been positively linked to white matter integrity. However, the shared effects of current social support and CM and their association with structural connectivity remain to be investigated. They might shed new light on the neurobiological basis of the protective mechanism of social support. Using connectome-based predictive modeling (CPM), we analyzed structural connectomes of N = 904 healthy adults derived from diffusion-weighted imaging. CPM predicts phenotypes from structural connectivity through a cross-validation scheme. Distinct and shared networks of white matter tracts predicting childhood trauma questionnaire scores and the social support questionnaire were identified. Additional analyses were applied to assess the stability of the results. CM and social support were predicted significantly from structural connectome data (all rs ≥ 0.119, all ps ≤ 0.016). Edges predicting CM and social support were inversely correlated, i.e., positively correlated with CM and negatively with social support, and vice versa, with a focus on frontal and temporal regions including the insula and superior temporal lobe. CPM reveals the predictive value of the structural connectome for CM and current social support. Both constructs are inversely associated with connectivity strength in several brain tracts. While this underlines the interconnectedness of these experiences, it suggests social support acts as a protective factor following adverse childhood experiences, compensating for brain network alterations. Future longitudinal studies should focus on putative moderating mechanisms buffering these adverse experiences.

The role of brain white matter in depression resilience and response to sleep interventions

Insomnia poses a high risk for depression. Brain mechanisms of sleep and mood improvement following cognitive behavioural therapy for insomnia remain elusive. This longitudinal study evaluated whether (i) individual differences in baseline brain white matter microstructure predict improvements and (ii) intervention affects brain white matter microstructure. People meeting the Diagnostic and Statistical Manual of Mental Disorders-5 criteria for Insomnia Disorder (n = 117) participated in a randomized controlled trial comparing 6 weeks of no treatment with therapist-guided digital cognitive behavioural therapy for insomnia, circadian rhythm support or their combination (cognitive behavioural therapy for insomnia + circadian rhythm support). Insomnia Severity Index and Inventory of Depressive Symptomatology-Self Report were assessed at baseline and followed up at Weeks 7, 26, 39 and 52. Diffusion-weighted magnetic resonance images were acquired at baseline and Week 7. Skeletonized white matter tracts, fractional anisotropy and mean diffusivity were quantified both tract-wise and voxel-wise using tract-based spatial statistics. Analyses used linear and mixed effect models while correcting for multiple testing using false discovery rate and Bonferroni for correlated endpoint measures. Our results show the following: (i) tract-wise lower fractional anisotropy in the left retrolenticular part of the internal capsule at baseline predicted both worse progression of depressive symptoms in untreated participants and more improvement in treated participants (fractional anisotropy × any intervention, PFDR = 0.053, Pcorr = 0.045). (ii) Only the cognitive behavioural therapy for insomnia + circadian rhythm support intervention induced a trend-level mean diffusivity decrease in the right superior corona radiata (PFDR = 0.128, Pcorr = 0.108), and individuals with a stronger mean diffusivity decrease showed a stronger alleviation of insomnia (R = 0.20, P = 0.035). In summary, individual differences in risk and treatment-supported resilience of depression involve white matter microstructure. Future studies could target the role of the left retrolenticular part of the internal capsule and right superior corona radiata and the brain areas they connect.

Human and chimpanzee shared and divergent neurobiological systems for general and specific cognitive brain functions

A long-standing topic of interest in human neurosciences is the understanding of the neurobiology underlying human cognition. Less commonly considered is to what extent such systems may be shared with other species. We examined individual variation in brain connectivity in the context of cognitive abilities in chimpanzees (n = 45) and humans in search of a conserved link between cognition and brain connectivity across the two species. Cognitive scores were assessed on a variety of behavioral tasks using chimpanzee- and human-specific cognitive test batteries, measuring aspects of cognition related to relational reasoning, processing speed, and problem solving in both species. We show that chimpanzees scoring higher on such cognitive skills display relatively strong connectivity among brain networks also associated with comparable cognitive abilities in the human group. We also identified divergence in brain networks that serve specialized functions across humans and chimpanzees, such as stronger language connectivity in humans and relatively more prominent connectivity between regions related to spatial working memory in chimpanzees. Our findings suggest that core neural systems of cognition may have evolved before the divergence of chimpanzees and humans, along with potential differential investments in other brain networks relating to specific functional specializations between the two species.