Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity.

BACKGROUND: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample. METHODS: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. RESULTS: The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = -0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05). LIMITATIONS: This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression). CONCLUSIONS: This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

Understanding the association between fatigue and neurocognitive functioning in patients with glioma: A cross-sectional multinational study.

Background: Fatigue and neurocognitive impairment are highly prevalent in patients with glioma, significantly impacting health-related quality of life. Despite the presumed association between these two factors, evidence remains sparse. Therefore, we aimed to investigate this relationship using multinational data. Methods: We analyzed data on self-reported fatigue and neurocognitive outcomes from postoperative patients with glioma from the University of California San Francisco (n = 100, UCSF) and Amsterdam University Medical Center (n = 127, Amsterdam UMC). We used multiple linear regression models to assess associations between fatigue and seven (sub)domains of neurocognitive functioning and latent profile analysis to identify distinct patterns of fatigue and neurocognitive functioning. Results: UCSF patients were older (median age 49 vs. 43 years, P = .002), had a higher proportion of grade 4 tumors (32% vs. 18%, P = .03), and had more neurocognitive deficits (P = .01). While the number of clinically fatigued patients was similar between sites (64% vs. 58%, P = .12), fatigue and the number of impaired neurocognitive domains were not correlated (P = .16-.72). At UCSF, neurocognitive domains were not related to fatigue, and at Amsterdam UMC attention and semantic fluency explained only 4-7% of variance in fatigue. Across institutions, we identified four distinct patterns of neurocognitive functioning, which were not consistently associated with fatigue. Conclusions: Although individual patients might experience both fatigue and neurocognitive impairment, the relationship between the two is weak. Consequently, both fatigue and neurocognitive functioning should be independently assessed and treated with targeted therapies.

Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants.

Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.

The relationship between pathological brain activity and functional network connectivity in glioma patients.

PURPOSE: Glioma is associated with pathologically high (peri)tumoral brain activity, which relates to faster progression. Functional connectivity is disturbed locally and throughout the entire brain, associating with symptomatology. We, therefore, investigated how local activity and network measures relate to better understand how the intricate relationship between the tumor and the rest of the brain may impact disease and symptom progression. METHODS: We obtained magnetoencephalography in 84 de novo glioma patients and 61 matched healthy controls. The offset of the power spectrum, a proxy of neuronal activity, was calculated for 210 cortical regions. We calculated patients' regional deviations in delta, theta and lower alpha network connectivity as compared to controls, using two network measures: clustering coefficient (local connectivity) and eigenvector centrality (integrative connectivity). We then tested group differences in activity and connectivity between (peri)tumoral, contralateral homologue regions, and the rest of the brain. We also correlated regional offset to connectivity. RESULTS: As expected, patients' (peri)tumoral activity was pathologically high, and patients showed higher clustering and lower centrality than controls. At the group-level, regionally high activity related to high clustering in controls and patients alike. However, within-patient analyses revealed negative associations between regional deviations in brain activity and clustering, such that pathologically high activity coincided with low network clustering, while regions with 'normal' activity levels showed high network clustering. CONCLUSION: Our results indicate that pathological activity and connectivity co-localize in a complex manner in glioma. This insight is relevant to our understanding of disease progression and cognitive symptomatology.

Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications.

Background: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain networks to parse the heterogeneity of depressive symptomatology in a large adolescent sample. Methods: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1,317 adolescents (52.49% female, mean±SD age=18.5±0.72). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS symptom/item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. Results: The network based on individual symptom scores revealed associations between cortical thickness measures and specific symptoms, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor.=-0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05). Limitations: This cross-sectional study included participants who were relatively healthy and relied on the self-reported assessment of depression symptoms. Conclusions: This study showcases the utility of network models in parsing heterogeneity in depressive symptoms, linking individual symptoms to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

Multiscale network neuroscience in neuro-oncology: How tumors, brain networks, and behavior connect across scales.

Network neuroscience refers to the investigation of brain networks across different spatial and temporal scales, and has become a leading framework to understand the biology and functioning of the brain. In neuro-oncology, the study of brain networks has revealed many insights into the structure and function of cells, circuits, and the entire brain, and their association with both functional status (e.g., cognition) and survival. This review connects network findings from different scales of investigation, with the combined aim of informing neuro-oncological healthcare professionals on this exciting new field and also delineating the promising avenues for future translational and clinical research that may allow for application of network methods in neuro-oncological care.

Multimodal multilayer network centrality relates to executive functioning.

Executive functioning (EF) is a higher order cognitive process that is thought to depend on a network organization facilitating integration across subnetworks, in the context of which the central role of the fronto-parietal network (FPN) has been described across imaging and neurophysiological modalities. However, the potentially complementary unimodal information on the relevance of the FPN for EF has not yet been integrated. We employ a multilayer framework to allow for integration of different modalities into one 'network of networks.' We used diffusion MRI, resting-state functional MRI, MEG, and neuropsychological data obtained from 33 healthy adults to construct modality-specific single-layer networks as well as a single multilayer network per participant. We computed single-layer and multilayer eigenvector centrality of the FPN as a measure of integration in this network and examined their associations with EF. We found that higher multilayer FPN centrality, but not single-layer FPN centrality, was related to better EF. We did not find a statistically significant change in explained variance in EF when using the multilayer approach as compared to the single-layer measures. Overall, our results show the importance of FPN integration for EF and underline the promise of the multilayer framework toward better understanding cognitive functioning.

Connecting Neurobiological Features with Interregional Dysconnectivity in Social-Cognitive Impairments of Schizophrenia.

Schizophrenia (SZ) is a devastating psychiatric disorder affecting about 1% of the world's population. Social-cognitive impairments in SZ prevent positive social interactions and lead to progressive social withdrawal. The neurobiological underpinnings of social-cognitive symptoms remain poorly understood, which hinders the development of novel treatments. At the whole-brain level, an abnormal activation of social brain regions and interregional dysconnectivity within social-cognitive brain networks have been identified as major contributors to these symptoms. At the cellular and subcellular levels, an interplay between oxidative stress, neuroinflammation and N-methyl-D-aspartate receptor hypofunction is thought to underly SZ pathology. However, it is not clear how these molecular processes are linked with interregional dysconnectivity in the genesis of social-cognitive symptoms. Here, we aim to bridge the gap between macroscale (connectivity analyses) and microscale (molecular and cellular mechanistic) knowledge by proposing impaired myelination and the disinhibition of local microcircuits as possible causative biological pathways leading to dysconnectivity and abnormal activity of the social brain. Furthermore, we recommend electroencephalography as a promising translational technique that can foster pre-clinical drug development and discuss attractive drug targets for the treatment of social-cognitive symptoms in SZ.

The longitudinal relation between executive functioning and multilayer network topology in glioma patients.

Many patients with glioma, primary brain tumors, suffer from poorly understood executive functioning deficits before and/or after tumor resection. We aimed to test whether frontoparietal network centrality of multilayer networks, allowing for integration across multiple frequencies, relates to and predicts executive functioning in glioma. Patients with glioma (n = 37) underwent resting-state magnetoencephalography and neuropsychological tests assessing word fluency, inhibition, and set shifting before (T1) and one year after tumor resection (T2). We constructed binary multilayer networks comprising six layers, with each layer representing frequency-specific functional connectivity between source-localized time series of 78 cortical regions. Average frontoparietal network multilayer eigenvector centrality, a measure for network integration, was calculated at both time points. Regression analyses were used to investigate associations with executive functioning. At T1, lower multilayer integration (p = 0.017) and epilepsy (p = 0.006) associated with poorer set shifting (adj. R2 = 0.269). Decreasing multilayer integration (p = 0.022) and not undergoing chemotherapy at T2 (p = 0.004) related to deteriorating set shifting over time (adj. R2 = 0.283). No significant associations were found for word fluency or inhibition, nor did T1 multilayer integration predict changes in executive functioning. As expected, our results establish multilayer integration of the frontoparietal network as a cross-sectional and longitudinal correlate of executive functioning in glioma patients. However, multilayer integration did not predict postoperative changes in executive functioning, which together with the fact that this correlate is also found in health and other diseases, limits its specific clinical relevance in glioma.

Cortical thickness and neurocognitive performance in former high-level female soccer and non-contact sport athletes.

BACKGROUND: Long-term effects of playing soccer (football) on the brain structure and function of the brain are vividly debated. While some studies showed differences in neurocognitive performance and structural brain changes in retired male players, data on female players are scarce. The present study compares cortical thickness and neurocognitive performance in former high-level female soccer (SOC) and non-contact sport athletes (CON). METHODS: 3 T T1-weighted 3D MPRAGE MRI was performed, and vertex-wise cortical thickness was analyzed using FreeSurfer (v. 6.0.0). Neurocognitive performance in seven domains of SOC and CON was assessed. A multivariate linear model was used to analyze interactions with respect to heading frequency and a history of concussion. RESULTS: SOC (n = 15, mean age 38.3 ± 5.1 years) and CON (n = 16, mean age 36.6 ± 5.8 years) had a similar cortical thickness and performed similarly in the neurocognitive tests except for verbal memory and psychomotor speed, where SOC performed significantly worse than CON. Moderate headers had a significantly larger cortical thickness than rare headers in the right inferior parietal region. Visual memory and cortical thickness were positively correlated in the group of frequent headers and negatively correlated in CON, but not in the other header groups. PERSPECTIVE: In contrast to previous reports in male soccer players, female players did not reveal cortical thinning in comparison with control athletes, whereas neurocognitive profiles of female soccer players might not significantly differ from male athletes. Small sample sizes, subjective header assessment, and the case-control study design require a cautious interpretation.

Frequency and burden of potentially treatable symptoms in glioma patients with stable disease.

Background & aims: Glioma patients experience a multitude of symptoms that negatively affect their health-related quality of life. Symptoms vary greatly across disease phases, and the patients' stable phase might be particularly suitable for assessing and treating symptoms. Identifying symptoms and patients' needs is a first step toward improving patient care. In glioma patients with stable disease, we assessed the frequency and burden of patient-reported symptoms, examined how these symptoms co-occur, and also determined whether patients would consider treatment to ameliorate specific symptoms. Methods: In this retrospective study, patients rated the frequency and burden of seventeen symptoms on a seven-point Likert scale and stated whether they would consider treatment for these symptoms. Correlations between frequency, burden, and considering treatment were evaluated with Kendall's Tau correlation coefficients. Based on partial correlations between symptom frequencies we visualized the symptoms as a network. Results: Fifty-two glioma patients with stable disease were included (31 WHO grade II/III, 21 WHO grade IV). The top five symptoms were fatigue, memory problems, reduced physical fitness, concentration problems, and drowsiness. Fatigue had the highest median frequency (4.5, interquartile range 2.5). Over half of the patients experienced three or more symptoms simultaneously and associations between all symptoms were depicted as a network. Overall, 35% of patients would consider treatment for at least one symptom. The wish to undergo symptom treatment correlated only moderately with symptom frequency and burden (range of correlations 0.24-0.57 and 0.28-0.61, respectively). Conclusion: Glioma patients with stable disease experience multiple symptoms with a consequently high symptom burden. Despite the high prevalence of symptoms, the inclination for symptom management interventions was relatively low. The most frequent and burdensome symptoms and the way they are interrelated could serve as a roadmap for future research on symptom management in these patients.

Cannabinoids to Improve Health-Related Quality of Life in Patients with Neurological or Oncological Disease: A Meta-Analysis.

Background: Cannabinoids have been suggested to alleviate frequently experienced symptoms of reduced mental well-being such as anxiety and depression. Mental well-being is an important subdomain of health-related quality of life (HRQoL). Reducing symptoms and maintaining HRQoL are particularly important in malignant primary brain tumor patients, as treatment options are often noncurative and prognosis remains poor. These patients frequently report unprescribed cannabinoid use, presumably for symptom relieve. As studies on brain tumor patients specifically are lacking, we performed a meta-analysis of the current evidence on cannabinoid efficacy on HRQoL and mental well-being in oncological and neurological patients. Methods: We performed a systematic PubMed, PsychINFO, Embase, and Web of Science search according to PRISMA guidelines on August 2 and 3, 2021. We included randomized controlled trials (RCTs) that assessed the effects of tetrahydrocannabinol (THC) or cannabidiol (CBD) on general HRQoL and mental well-being. Pooled effect sizes were calculated using Hedges g. Risk of bias of included studies was assessed using Cochrane's Risk of Bias tool. Results: We included 17 studies: 4 in oncology and 13 in central nervous system (CNS) disease. Meta-analysis showed no effect of cannabinoids on general HRQoL (g=-0.02 confidence interval [95% CI -0.11 to 0.06]; p=0.57) or mental well-being (g=-0.02 [95% CI -0.16 to 0.13]; p=0.81). Conclusions: RCTs in patients with cancer or CNS disease showed no effect of cannabinoids on HRQoL or mental well-being. However, studies were clinically heterogeneous and since many glioma patients currently frequently use cannabinoids, future studies are necessary to evaluate its value in this specific population.

Long-term wellbeing and neurocognitive functioning of diffuse low-grade glioma patients and their caregivers: A longitudinal study spanning two decades.

BACKGROUND: While patients with diffuse low-grade glioma (LGG) often survive for years, there is a risk of tumor progression which may impact patients' long-term health-related quality of life (HRQOL) and neurocognitive functioning (NCF). We present a follow-up of LGG patients and their informal caregivers (T3) who took part in our previous HRQOL investigations (T1, M = 7 and T2 M = 13 years after diagnosis). METHODS: Participants completed HRQOL (short form-36 health survey [SF-36]; EORTC-BN20), fatigue (Checklist Individual Strength [CIS]), and depression (Center for Epidemiological Studies-Depression [CES-D]) questionnaires and underwent NCF assessments. T3 scores were compared with matched controls. Changes over time (T1-T2-T3) on group and participant level were assessed. Where available, histology of the initial tumor was revised and immunohistochemical staining for IDH1 R132H mutant protein was performed. RESULTS: Thirty patients and nineteen caregivers participated. Of N = 11 with tissue available, 3 patients had confirmed diffuse LGG. At T3, patients (M = 26 years after diagnosis) had HRQOL and NCF similar to, or better than controls, yet 23.3% and 53.3% scored above the cut-off for depression (≥16 CES-D) and fatigue (≥35 CIS), respectively. Caregivers' HRQOL was similar to controls but reported high rates of fatigue (63.2%). Over time, patients' mental health improved (P < .05). Minimal detectable change in HRQOL over time was observed in individual patients (30% improvement; 23.3% decline; 20% both improvement and decline) with 23.3% remaining stable. NCF remained stable or improved in 82.8% of patients. CONCLUSIONS: While HRQOL and NCF do not appear greatly impacted during long-term survivorship in LGG, depressive symptoms and fatigue are persistent.

Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis.

BACKGROUND: Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS). OBJECTIVE: This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS. METHODS: Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time. RESULTS: In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors). CONCLUSION: Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.

Toward unraveling the correlates of fatigue in glioma.

Background: Even though fatigue is one of the most prevalent and burdensome symptoms in patients with glioma, its etiology and determinants are still poorly understood. We aimed to identify which demographic, tumor- and treatment-related characteristics and patient-reported outcome measures (PROMs) are associated with or are predictors of fatigue in glioma. Methods: In this retrospective observational study, we included glioma patients with preoperative and postoperative assessments including PROMs on fatigue, depression, cognitive functioning, and health-related quality of life (HRQoL). Linear mixed models were used to identify which clinical factors and PROMs were associated with fatigue and linear multiple regression was used to detect predictors of postoperative fatigue. Results: In this study, 222 patients were included (78% grade II-III glioma, 22% grade IV). These patients had performed 333 assessments (193 preoperative and 116 one year postoperatively). Of all assessments, 39% was indicative of severe fatigue. Several HRQoL domains, depression, and right-sided tumors were significantly associated with fatigue (marginal R 2 = 0.63). Contrary to common expectations, tumor type, treatment-related factors, and timing of the assessment, were not associated with fatigue. In a subgroup of 70 patients with follow-up assessments, preoperative fatigue, and physical functioning were predictors of postoperative fatigue (adjusted R 2 = 0.31). Conclusion: Fatigue is a complex symptom, which should not solely be attributed to the tumor or its treatment, but is instead related to different aspects of mood and HRQoL. These insights are important in understanding fatigue and could guide symptom management, especially in patients with lower-grade tumors.

Regional healthy brain activity, glioma occurrence and symptomatology.

It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients' tumour locations relates to tumour and patient characteristics. Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients' individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics. As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients' individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients. In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients' individual tumour locations may capture both tumour biology and patients' performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of 'cancer neuroscience'.

Effects of a single-dose methylphenidate challenge on resting-state functional connectivity in stimulant-treatment naive children and adults with ADHD.

Prior studies suggest that methylphenidate, the primary pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD), alters functional brain connectivity. As the neurotransmitter systems targeted by methylphenidate undergo significant alterations throughout development, the effects of methylphenidate on functional connectivity may also be modulated by age. Therefore, we assessed the effects of a single methylphenidate challenge on brain network connectivity in stimulant-treatment naïve children and adults with ADHD. We obtained resting-state functional MRI from 50 boys (10-12 years of age) and 49 men (23-40 years of age) with ADHD (DSM IV, all subtypes), before and after an oral challenge with 0.5 mg/kg methylphenidate; and from 11 boys and 12 men as typically developing controls. Connectivity strength (CS), eigenvector centrality (EC), and betweenness centrality (BC) were calculated for the striatum, thalamus, dorsal anterior cingulate cortex (dACC), and prefrontal cortex (PFC). In line with our hypotheses, we found that methylphenidate decreased measures of connectivity and centrality in the striatum and thalamus in children with ADHD, but increased the same metrics in adults with ADHD. Surprisingly, we found no major effects of methylphenidate in the dACC and PFC in either children or adults. Interestingly, pre-methylphenidate, participants with ADHD showed aberrant connectivity and centrality compared to controls predominantly in frontal regions. Our findings demonstrate that methylphenidate's effects on connectivity of subcortical regions are age-dependent in stimulant-treatment naïve participants with ADHD, likely due to ongoing maturation of dopamine and noradrenaline systems. These findings highlight the importance for future studies to take a developmental perspective when studying the effects of methylphenidate treatment.