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Objective: Autism spectrum disorder (ASD) is a neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long- relative to short-range functional connections. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes., Methods: We studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean ± SD age = 20.8 ± 8.1 years) and 108 neurotypical controls (NT, 19.2 ± 7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types., Results: Compared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores., Limitations: Despite rigorous harmonization across the three different acquisition sites, heterogeneity in autism poses a potential limitation to the generalizability of our results. Additionally, we focussed male participants, warranting future studies in more balanced cohorts., Conclusions: Our study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity., (© 2024. The Author(s).)

Complex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology ( p ) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers., Competing Interests: Declarations of interest: None

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by the use of sleep aids and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

Objective: Temporal lobe epilepsy (TLE) is typically associated with pathology of the hippocampus, a key structure involved in relational memory, including episodic, semantic, and spatial memory processes. While it is widely accepted that TLE-associated hippocampal alterations underlie memory deficits, it remains unclear whether impairments relate to a specific cognitive domain or multiple ones., Methods: We administered a recently validated task paradigm to evaluate episodic, semantic, and spatial memory in 24 pharmacoresistant TLE patients and 50 age- and sex-matched healthy controls. We carried out two-way analyses of variance to identify memory deficits in individuals with TLE relative to controls across different relational memory domains, and used partial least squares correlation to identify factors contributing to variations in relational memory performance across both cohorts., Results: Compared to controls, TLE patients showed marked impairments in episodic and spatial memory, with mixed findings in semantic memory. Even when additionally controlling for age, sex, and overall cognitive function, between-group differences persisted along episodic and spatial domains. Moreover, age, diagnostic group, and hippocampal volume were all associated with relational memory behavioral phenotypes., Significance: Our behavioral findings show graded deficits across relational memory domains in people with TLE, which provides further insights into the complex pattern of cognitive impairment in the condition., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms., Competing Interests: Declaration of Competing Interest All authors declare no conflicts of interest., (Copyright © 2023. Published by Elsevier Inc.)

It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability., (© 2022. The Author(s).)

Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity., Competing Interests: The authors have declared that no competing interests exist.

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood., (© 2022. The Author(s).)

The human mind wanders spontaneously and frequently, revisiting the past and imagining the future of self and of others. External and internal factors can influence wandering spontaneous thoughts, whose content predicts subsequent emotional states. We propose that social imitation, an action that increases well-being and closeness by poorly understood mechanisms, impacts behavioural states in part by modulating post-imitation mind-wandering. In 43 young subjects, we find that imitating the arm movements of an actor alters the dynamics and the content of subsequent resting-state spontaneous thoughts. Imitation-sensitive features of spontaneous thoughts correlate with behavioural states and personality traits. EEG microstate analysis reveals that global patterns of correlated neuronal activity predict imitation-induced changes in spontaneous thoughts. Exploratory analyses indicate a possible modulatory effect of social imitation via the endogenous release of oxytocin. Thus, social imitation can induce selective modulations of ongoing activity in specific neural networks to change spontaneous thought patterns as a function of personality traits, and to ultimately orchestrate behavioural states., (Copyright © 2022. Published by Elsevier Inc.)

Emotion dysregulation is central to the development and maintenance of psychopathology, and is common across many psychiatric disorders. Neurobiological models of emotion dysregulation involve the fronto-limbic brain network, including in particular the amygdala and prefrontal cortex (PFC). Neural variability has recently been suggested as an index of cognitive flexibility. We hypothesized that within-subject neural variability in the fronto-limbic network would be related to inter-individual variation in emotion dysregulation in the context of low affective control. In a multi-site cohort (N = 166, 93 females) of healthy individuals and individuals with emotional dysregulation (attention deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), and borderline personality disorder (BPD)), we applied partial least squares (PLS), a multivariate data-driven technique, to derive latent components yielding maximal covariance between blood-oxygen level-dependent (BOLD) signal variability at rest and emotion dysregulation, as expressed by affective lability, depression and mania scores. PLS revealed one significant latent component (r = 0.62, p = 0.044), whereby greater emotion dysregulation was associated with increased neural variability in the amygdala, hippocampus, ventromedial, dorsomedial and dorsolateral PFC, insula and motor cortex, and decreased neural variability in occipital regions. This spatial pattern bears a striking resemblance to the fronto-limbic network, which is thought to subserve emotion regulation, and is impaired in individuals with ADHD, BD, and BPD. Our work supports emotion dysregulation as a transdiagnostic dimension with neurobiological underpinnings that transcend diagnostic boundaries, and adds evidence to neural variability being a relevant proxy of neural efficiency., (© 2021. The Author(s).)

The corpus callosum is the largest white matter pathway in the brain connecting the two hemispheres. In the context of developmental absence (agenesis) of the corpus callosum (AgCC), a proposed candidate for neuroplastic response is strengthening of intrahemispheric pathways. To test this hypothesis, we assessed structural and functional connectivity in a uniquely large cohort of children with AgCC (n = 20) compared with typically developing controls (TDC, n = 29), and then examined associations with neurobehavioral outcomes using a multivariate data-driven approach (partial least squares correlation, PLSC). For structural connectivity, children with AgCC showed a significant increase in intrahemispheric connectivity in addition to a significant decrease in interhemispheric connectivity compared with TDC, in line with the aforementioned hypothesis. In contrast, for functional connectivity, children with AgCC and TDC showed a similar pattern of intrahemispheric and interhemispheric connectivity. In conclusion, we observed structural strengthening of intrahemispheric pathways in children born without corpus callosum, which seems to allow for functional connectivity comparable to a typically developing brain, and were relevant to explain neurobehavioral outcomes in this population. This neuroplasticity might be relevant to other disorders of axonal guidance, and developmental disorders in which corpus callosum alteration is observed., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Agenesis of the corpus callosum (AgCC) is a congenital brain malformation characterized by the complete or partial failure to develop the corpus callosum. Despite missing the largest white matter bundle connecting the left and right hemispheres of the brain, studies have shown preserved inter-hemispheric communication in individuals with AgCC. It is likely that plasticity provides mechanisms for the brain to adjust in the context of AgCC, as the malformation disrupts programmed developmental brain processes very early on. A proposed candidate for neuroplastic response in individuals with AgCC is strengthening of intra-hemispheric structural connections. In the present study, we explore this hypothesis using a graph-based approach of the structural connectome, which enables intra- and inter-hemispheric analyses at multiple resolutions and quantification of structural characteristics through graph metrics. Structural graph metrics of 19 children with AgCC (13 with complete, 6 with partial AgCC) were compared to those of 29 typically developing controls (TDC). Associations between structural graph metrics and a wide range of neurobehavioral outcomes were examined using a multivariate data-driven approach (Partial Least Squares Correlation, PLSC). Our results provide new evidence suggesting structural strengthening of intra-hemispheric pathways as a neuroplastic response in the acallosal brain, and highlight regional variability in structural connectivity in children with AgCC compared to TDC. There was little evidence that structural graph properties in children with AgCC were associated with neurobehavioral outcomes. To our knowledge, this is the first report leveraging graph theory tools to explicitly characterize whole-brain intra- and inter-hemispheric structural connectivity in AgCC, opening avenues for future research on neuroplastic responses in AgCC., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses. Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics. Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data. Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes, and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thereby neglecting the potential influence of huge swaths of the brain., Methods: A multivariate data-driven approach (partial least squares) was used to identify latent components linking a large set of clinical, cognitive, and personality measures to whole-brain resting-state functional connectivity patterns across 224 participants. The participants were either healthy (n = 110) or diagnosed with bipolar disorder (n = 40), attention-deficit/hyperactivity disorder (n = 37), schizophrenia (n = 29), or schizoaffective disorder (n = 8). In contrast to traditional case-control analyses, the diagnostic categories were not used in the partial least squares analysis but were helpful for interpreting the components., Results: Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction, and impulsivity. Each component was associated with a unique whole-brain resting-state functional connectivity signature and was shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network and its connectivity with subcortical structures and cortical executive networks., Conclusions: We identified three distinct dimensions with dissociable (but overlapping) whole-brain resting-state functional connectivity signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Motor functional neurological disorder (mFND) is a clinical diagnosis with reliable features; however, patients are reluctant to accept the diagnosis and physicians themselves bear doubts on potential misdiagnoses. The identification of a positive biomarker could help limiting unnecessary costs of multiple referrals and investigations, thus promoting early diagnosis and allowing early engagement in appropriate therapy., Objectives: To test whether resting-state (RS) functional magnetic resonance imaging could discriminate patients suffering from mFND from healthy controls., Methods: We classified 23 mFND patients and 25 age- and gender-matched healthy controls based on whole-brain RS functional connectivity (FC) data, using a support vector machine classifier and the standard Automated Anatomic Labeling (AAL) atlas, as well as two additional atlases for validation., Results: Accuracy, specificity and sensitivity were over 68% (p = 0.004) to discriminate between mFND patients and controls, with consistent findings between the three tested atlases. The most discriminative connections comprised the right caudate, amygdala, prefrontal and sensorimotor regions. Post-hoc seed connectivity analyses showed that these regions were hyperconnected in patients compared to controls., Conclusions: The good accuracy to discriminate patients from controls suggests that RS FC could be used as a biomarker with high diagnostic value in future clinical practice to identify mFND patients at the individual level.

The posterior parietal cortex (PPC) is an anatomically heterogeneous brain region implicated in a wide range of cognitive operations, including egocentric spatial processing and both short- and long-term memory. Here, we report functional specificities of cytoarchitectonically defined subregions of PPC during the processing of scenes across changes in viewpoint. Participants (n = 16) saw photographs of familiar and unfamiliar places while undergoing functional magnetic resonance imaging (fMRI). On each trial, 4 viewpoints of the same place were presented, with either a plausible sequence of viewpoints (SEQ) or a scrambled order (SCRA). Distinct response profiles were observed within PPC. Area 7A showed increased activity for SEQ versus SCRA order, regardless of place familiarity, whereas the rostral inferior parietal lobule showed preferential increases for unfamiliar versus familiar places in SEQ series. In contrast, more posterior subregions in both superior and inferior PPC exhibited increases for familiar versus unfamiliar places at the end of the sequence, regardless of order. The data highlight the distinctive contribution of several subregions of PPC during the processing of scenes, with specific cortical areas involved in the progressive integration of spatial information across viewpoint changes, and others involved in the retrieval and maintenance of scene information in memory., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

The parahippocampal cortex (PHC) participates in both perception and memory. However, the way perceptual and memory processes cooperate when we navigate in our everyday life environment remains poorly understood. We studied a stroke patient presenting a brain lesion in the right PHC, which resulted in a mild and quantifiable topographic agnosia, and allowed us to investigate the role of this structure in overt place recognition. Photographs of personally familiar and unfamiliar places were displayed during functional magnetic resonance imaging (fMRI). Familiar places were either recognized or unrecognized by the patient and 6 age- and education-matched controls in a visual post-scan recognition test. In fMRI, recognized places were associated with a network comprising the fusiform gyrus in the intact side, but also the right anterior PHC, which included the lesion site. Moreover, this right PHC showed increased connectivity with the left homologous PHC in the intact hemisphere. By contrasting recognized with unrecognized familiar places, we replicate the finding of the joint involvement of the retrosplenial cortex, occipito-temporal areas, and posterior parietal cortex in place recognition. This study shows that the ability for left and right anterior PHC to communicate despite the neurological damage conditioned place recognition success in this patient. It further highlights a hemispheric asymmetry in this process, by showing the fundamental role of the right PHC in topographic agnosia.

Background: Cerebrovascular disease and neurodegeneration cause cognitive impairment and frequently coexist., Aims: Our objectives were to investigate the prevalence and cognitive impact of medial temporal lobe atrophy - a radiological marker often associated with Alzheimer's disease - in a hospital stroke service., Methods: Retrospective cohort study of patients from a hospital stroke service. Patients assessed for suspected ischemic stroke or transient ischemic attack, irrespective of final diagnosis, underwent neuropsychological testing and magnetic resonance imaging. medial temporal lobe atrophy, white matter hyperintensities, lacunes, and cerebral microbleeds were rated using established criteria and validated scales. The associations between medial temporal lobe atrophy and cognition were tested using multivariable logistic regression analyses, adjusted for age and imaging markers of cerebrovascular disease., Results: Three hundred and ninety-three patients were included, of whom 169 (43%; 95% confidence interval: 38·1-48·1%) had medial temporal lobe atrophy; in 38 patients (9·7%), medial temporal lobe atrophy was severe (mean score ≥2). In unadjusted logistic regression analyses in the whole cohort, mean medial temporal lobe atrophy score was associated with verbal memory, nominal and perceptual skills, executive function, and speed and attention. After adjustment for age, white matter hyperintensities, number of lacunes, presence of cerebral microbleeds, previous ischemic stroke or transient ischemic attack, and premorbid intelligence quotient, mean medial temporal lobe atrophy score remained associated with impairment in verbal memory (odds ratio: 1·64; 95% confidence interval 1·04-2·58) and nominal skills (odds ratio: 1·61; 95% confidence interval 1·04-2·48)., Conclusions: Medial temporal lobe atrophy is common and has an independent impact on cognitive function in a stroke service population, independent of confounding factors including age and magnetic resonance imaging markers of cerebrovascular disease. Medial temporal lobe atrophy is independently related to verbal memory and nominal skills, while small vessel pathology also contributes to speed and attention, and executive and perceptual functions., (© 2015 World Stroke Organization.)

Background/aims: Atrophy in both grey and white matter is found in normal aging. The prefrontal cortex and the frontal lobe white matter are thought to be the most affected regions. Our aim was to examine the effects of normal aging on cortical grey matter using a 3D quantitative cortical mapping method., Methods: We analyzed 1.5-tesla brain magnetic resonance imaging data from 44 cognitively normal elderly subjects using cortical pattern matching and cortical thickness analyses. Linear regression analysis was used to study the effect of age on cortical thickness. 3D map-wide correction for multiple comparisons was conducted with permutation analyses using a threshold of p < 0.01., Results: We found a significant negative association between age and cortical thickness in the right hemisphere (pcorrected = 0.009) and a trend level association in the left hemisphere (pcorrected = 0.081). Age-related changes were greatest in the sensorimotor, bilateral dorsal anterior cingulate and supplementary motor cortices, and the right posterior middle and inferior frontal gyri. Age effects greater in the medial than lateral visual association cortices were also seen bilaterally., Conclusion: Our novel method further validates that normal aging results in diffuse cortical thinning that is most pronounced in the frontal and visual association cortices.