Brain Connectivity Research Articles

Neurobiological Changes Induced by Mindfulness and Meditation: A Systematic Review

Background and Objectives: Meditation and mindfulness, rooted in ancient traditions, enhance mental well-being by cultivating awareness and emotional control. It has been shown to induce neuroplasticity, increase cortical thickness, reduce amygdala reactivity, and improve brain connectivity and neurotransmitter levels, leading to improved emotional regulation, cognitive function, and stress resilience. This systematic review will synthesize research on neurobiological changes associated with mindfulness and meditation practices. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, Cochrane Library, and Embase databases without any search time range. This review has been registered on Open OSF (n) GV2JY. Results: Mindfulness-Based Stress Reduction (MBSR) enhances brain regions related to emotional processing and sensory perception, improves psychological outcomes like anxiety and depression, and exhibits unique mechanisms of pain reduction compared to placebo. Conclusions: This review highlights that mindfulness, particularly through MBSR, improves emotional regulation and brain structure, reduces anxiety, and enhances stress resilience. Future research should focus on diverse populations and naturalistic settings to better understand and optimize these benefits.

The different impacts of functional network centrality and connectivity on the complexity of brain signals in healthy control and first-episode drug-naïve patients with major depressive disorder.

In recent years, brain signal complexity has gained attention as an indicator of brain well-being and a predictor of disease and dysfunction. Brain entropy quantifies this complexity. Assessment of functional network centrality and connectivity reveals that information communication induces neural signal oscillations in certain brain regions. However, their relationship is uncertain. This work studied brain signal complexity, network centrality, and connectivity in both healthy and depressed individuals. The current work comprised a sample of 124 first-episode drug-naïve patients with major depressive disorder (MDD) and 105 healthy controls (HC). Six functional networks were created for each person using resting-state functional magnetic resonance imaging. For each network, entropy, centrality, and connectivity were computed. Using structural equation modeling, this study examined the associations between brain network entropy, centrality, and connectivity. The findings demonstrated substantial correlations of entropy with both centrality and connectivity in HC and these correlation patterns were disrupted in MDD. Compared to HC, MDD exhibited higher entropy in four networks and demonstrated changes in centralities across all networks. The structural equation modeling showed that network centralities, connectivity, and depression severity had impacts on brain entropy. Nevertheless, no impacts were observed in the opposite directions. This study indicated that the complexity of brain signals was influenced not only by the interactions among different areas of the brain but also by the severity level of depression. These findings enhanced our comprehension of the associations of brain entropy with its influential factors.

Chronic Δ9-tetrahydrocannabinol exposure in adolescent nonhuman primates: persistent abnormalities in economic demand and brain functional connectivity.

Although chronic cannabis use during adolescence can alter brain function and impair complex behavioral processes, it is unclear whether such deficits persist into adulthood. Using a coordinated awake neuroimaging and behavioral approach in nonhuman primates, we addressed this issue by examining the impact of chronic adolescent exposure to Δ9-tetrahydrocannabinol (THC) on brain functional connectivity and motivational processes during early adulthood. Female and male squirrel monkeys (n = 23) were treated daily for 6 months during adolescence with vehicle or either a low (0.32 mg/kg) or high dose (3.2 mg/kg) of THC. Regional homogeneity and seed-to-whole-brain functional connectivity were analyzed prior to, during, and following discontinuation of chronic treatment to examine changes in regions implicated in reward processing. Subsequently, motivation and reward sensitivity in these subjects, now young adults, were evaluated in economic demand studies by determining the relationship between escalating response requirements and consumption of differing magnitudes of a palatable food reinforcer. Results show that adolescent THC exposure led to persistent alterations in mOFC, caudate, and ventral striatum whole-brain connectivity. Moreover, subjects treated with vehicle during adolescence displayed an orderly and expected inverse relationship between reward magnitude and demand elasticity, whereas THC-treated subjects exhibited dosage-dependent disorder in reward sensitivity and motivational deficits. Changes in neural circuitry (local connectivity in ventral striatum and whole brain connectivity in mOFC) and economic demand were correlated with indices of reward sensitivity in vehicle- but not THC-treated subjects. Taken together, these data indicate that chronic adolescent THC exposure produced long-lasting neurocognitive abnormalities in reward processing.

Can human brain connectivity explain verbal working memory?

ABSTRACT The ability of humans to store spoken words in verbal working memory and build extensive vocabularies is believed to stem from evolutionary changes in cortical connectivity across primate species. However, the underlying neurobiological mechanisms remain unclear. Why can humans acquire vast vocabularies, while non-human primates cannot? This study addresses this question using brain-constrained neural networks that realize between-species differences in cortical connectivity. It investigates how these structural differences support the formation of neural representations for spoken words and the emergence of verbal working memory, crucial for human vocabulary building. We develop comparative models of frontotemporal and occipital cortices, reflecting human and non-human primate neuroanatomy. Using meanfield and spiking neural networks, we simulate auditory word recognition and examine verbal working memory function. The “human models”, characterized by denser inter-area connectivity in core language areas, produced larger cell assemblies than the “monkey models”, with specific topographies reflecting semantic properties of the represented words. Crucially, longer-lasting reverberant neural activity was observed in human versus monkey architectures, compatible with robust verbal working memory, a necessary condition for vocabulary building. Our findings offer insights into the structural basis of human-specific symbol learning and verbal working memory, shedding light on humans’ unique capacity for large vocabulary acquisition.

Asymmetry of Directed Brain Connectivity at Birth in Low-Risk Full-Term Newborns.

Functional connectivity hubs were previously identified at the source level in low-risk full-term newborns by high-density electroencephalography (HD-EEG). However, the directionality of information flow among hubs remains unclear. The aim of this study was to study the directionality of information flow among source level hubs in low-risk full-term newborns using HD-EEG. A retrospective analysis of HD-EEG collected from a prospective study. Subjects included 112 low-risk full-term (37-41 weeks' gestation) newborns born in a large delivery center and studied within 72 hours of life by HD-EEG. The directionality of information flow between hubs at the source level was quantified using the partial directed coherence in the delta frequency band. Descriptive statistics were used to identify the maximum and minimum information flow. Differences in information flow between cerebral hemispheres were assessed using Student t-test. There was higher information flow from the left hemisphere to the right hemisphere hubs (p < 0.05, t-statistic = 2). The brainstem had the highest information inflow and lowest outflow among all the hubs. The left putamen received the lowest information, and the right pallidum had the highest information outflow to other hubs. In low-risk full-term newborns, there is a significant information flow asymmetry already present, with left hemisphere dominance at birth. The relationship between these findings and the more prevalent left hemisphere dominance observed in full-term newborns, particularly in relation to language, warrants further study.

Spatially integrated cortico-subcortical tracing data for analyses of rodent brain topographical organization.

The cerebral cortex extends axonal projections to several subcortical brain regions, including the striatum, thalamus, superior colliculus, and pontine nuclei. Experimental tract-tracing studies have shown that these subcortical projections are topographically organized, reflecting the spatial organization of sensory surfaces and body parts. Several public collections of mouse- and rat- brain tract-tracing data are available, with the Allen mouse brain connectivity atlas being most prominent. There, a large body of image data can be inspected, but it is difficult to combine data from different experiments and compare spatial distribution patterns. To enable co-visualization and comparison of topographical organization in mouse brain cortico-subcortical projections across experiments, we represent axonal labelling data as point data in a common 3D brain atlas space. We here present a collection of point-cloud data representing spatial distribution of corticostriatal, corticothalamic, corticotectal, and corticopontine projections in mice and exemplify how these spatially integrated point data can be used as references for experimental investigations of topographic organization in transgenic mice, and for cross-species comparison with corticopontine projections in rats.

Deriving comprehensive literature trends on multi-omics analysis studies in autism spectrum disorder using literature mining pipeline

Autism spectrum disorder (ASD) is characterized by highly heterogenous abnormalities in functional brain connectivity affecting social behavior. There is a significant progress in understanding the molecular and genetic basis of ASD in the last decade using multi-omics approach. Mining this large volume of biomedical literature for insights requires considerable amount of manual intervention for curation. Machine learning and artificial intelligence fields are advancing toward simplifying data mining from unstructured text data. Here, we demonstrate our literature mining pipeline to accelerate data to insights. Using topic modeling and generative AI techniques, we present a pipeline that can classify scientific literature into thematic clusters and can help in a wide array of applications such as knowledgebase creation, conversational virtual assistant, and summarization. Employing our pipeline, we explored the ASD literature, specifically around multi-omics studies to understand the molecular interplay underlying autism brain.

Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics.

Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject. These approaches, however, either do not accomodate a more general association between community structure and an outcome or cannot accommodate additional covariates that may confound the association of interest. We propose a semiparametric kernel machine regression model for either a continuous or binary outcome, where covariate effects are modeled parametrically and brain connectivity measures are measured nonparametrically. By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. We evaluate our proposed methodology on both simulated and real datasets.

The brain’s first “traffic map” through Unified Structural and Functional Connectivity (USFC) modeling

The brain’s white matter connections are thought to provide the structural basis for its functional connections between distant brain regions but how our brain selects the best structural routes for functional communications remains poorly understood. In this study, we propose a Unified Structural and Functional Connectivity (USFC) model and use an “economical assumption” to create the brain’s first “traffic map” reflecting how frequently each segment of the brain structural connection is used to achieve the global functional communication system. The resulting USFC map highlights regions in the subcortical, default-mode, and salience networks as the most heavily traversed nodes and a midline frontal-caudate-thalamus-posterior cingulate-visual cortex corridor as the backbone of the whole brain connectivity system. Our results further revealed a striking negative association between structural and functional connectivity strengths in routes supporting negative functional connections, as well as significantly higher efficiency metrics and better predictive performance for cognition in the USFC connectome when compared to structural and functional ones alone. Overall, the proposed USFC model opens up a new window for integrated brain connectome modeling and provides a major leap forward in brain mapping efforts for a better understanding of the brain’s fundamental communication mechanisms.

A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation

This study presents a method for generating synthetic electroencephalography (EEG) signals to test dynamic directed brain connectivity estimation methods. Current methods for evaluating dynamic brain connectivity estimation techniques face challenges due to the lack of ground truth in real EEG signals. To address this, we propose a framework for generating synthetic EEG signals with predefined dynamic connectivity changes. Our approach allows for evaluating and optimizing dynamic connectivity estimation methods, particularly Granger causality (GC). We demonstrate the framework’s utility by identifying optimal window sizes and regression orders for GC analysis. The findings could guide the development of more accurate dynamic connectivity techniques.

Functional connectivity and cognitive decline: a review of rs-fMRI, EEG, MEG, and graph theory approaches in aging and dementia

Age-related changes in the brain cause cognitive decline and dementia. In recent year’s researchers’ extensively studied the relationship between age related changes in functional connectivity (FC) in dementia. Those studies explore the alterations in FC patterns observed in aging and neurodegenerative disorders using techniques such as resting-state functional magnetic resonance imaging (rs-fMRI), electroencephalography (EEG) coherence analysis, and graph theory approaches. The current review summarizes the findings, which highlight the impact of FC changes on cognitive decline and neurodegenerative disease progression using these techniques and emphasize the importance of understanding neural alterations for early detection and intervention. The findings underscore the complexity of cognitive aging and the need for further research to differentiate normal aging from pathological conditions. rs-fMRI is essential for studying brain changes associated with aging and pathology by capturing coherent fluctuations in brain activity during rest, providing insights into FC without task-related confounds. Key networks such as the default mode network and front parietal control network are crucial in revealing age-related connectivity changes. Despite challenges like neurovascular uncoupling and data complexity, ongoing advancements promise improved clinical applications of rs-fMRI in understanding cognitive decline across the lifespan. EEG and magnetoencephalography (MEG) are cost-effective techniques with high temporal resolution, allowing detailed study of brain rhythms and FC. Recent studies highlight EEG/MEG’s potential in early Alzheimer’s disease detection by identifying changes in brain connectivity patterns. Integration of machine learning techniques enhances diagnostic accuracy, although further validation and research are necessary. Graph theory offers a quantitative framework to analyze cognitive networks, identifying distinct topological differences between healthy aging and pathological conditions. Future research should expand exploration into diverse neurodegenerative disorders beyond mild cognitive impairment, integrating neuroimaging techniques to refine diagnostic precision and deepen insights into brain function and connectivity.

Corrigendum to “Contributions of Coping Flexibility and Associated Functional Brain Connectivity to Resilient Trajectories of Mental Health During the COVID-19 Pandemic”

Corrigendum to “Contributions of Coping Flexibility and Associated Functional Brain Connectivity to Resilient Trajectories of Mental Health During the COVID-19 Pandemic”

BRAIN COMPUTER INTERFACE: THE IMPACT ON NEUROPLASTICITY AND MOTOR LEARNING-A NARRATIVE REVIEW.

Background: Neurological disorders, especially stroke, have now become the major causes of death all around the world. They are also the leading cause of impairments and disabilities among those who survive which leads to dependency in ADL’s and an impairment in overall quality of life. Even though traditional rehabilitation techniques and advances in technology (VR/AR/GAMING/ASSISTIVE DEVICES/EXOSKELETONS) play a major role in the recovery process, not all individuals show a 100% positive outcome. Research suggests that the combination of BCI with traditional rehab techniques or advanced technologies help in increasing neuroplasticity by translating the brain signals into commands that can control external devices thereby leading to improved functions and activities. Objective: The objective of this review is to summarize the available evidence and to highlight the mechanism through which the BCI’s promote neuroplasticity and enhance motor functions. Methods: Articles were searched and reviewed from PubMed, research gate, google scholar using the terms BCI, neuroplasticity, motor functions, neurorehabilitation etc. only the studies which were fully accessible; showed positive outcomes; focusing on motor recovery and neuro rehab were included and the studies which focused on other uses of BCI such as gaming, commercial applications and those that focused on measuring non motor functions and cognitive impairments were excluded. Results and Conclusion: After reviewing the articles it was concluded that BCI in combination with conventional rehab techniques and advanced technologies promote neuroplasticity thereby improving functions through repeated activation of neurons and improving coordination and motor function by enhancing the brain connectivity. Implications: The invention of BCI and its combination with other rehab processes aids in the improvement of motor recovery and an overall improvement in ADL’s and quality of life of patients suffering from various neurological disorders.

α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor density underlies intraregional and interregional functional centrality

Local and global functional connectivity densities (lFCD and gFCD, respectively), derived from functional magnetic resonance imaging (fMRI) data, represent the degree of functional centrality within local and global brain networks. While these methods are well-established for mapping brain connectivity, the molecular and synaptic foundations of these connectivity patterns remain unclear. Glutamate, the principal excitatory neurotransmitter in the brain, plays a key role in these processes. Among its receptors, the α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) is crucial for neurotransmission, particularly in cognitive functions such as learning and memory. This study aimed to examine the association of the AMPAR density and FCD metrics of intraregional and interregional functional centrality. Using [11C]K-2, a positron emission tomography (PET) tracer specific for AMPARs, we measured AMPAR density in the brains of 35 healthy participants. Our findings revealed a strong positive correlation between AMPAR density and both lFCD and gFCD-lFCD across the entire brain. This correlation was especially notable in key regions such as the anterior cingulate cortex, posterior cingulate cortex, pre-subgenual frontal cortex, Default Mode Network, and Visual Network. These results highlight that postsynaptic AMPARs significantly contribute to both local and global functional connectivity in the brain, particularly in network hub regions. This study provides valuable insights into the molecular and synaptic underpinnings of brain functional connectomes.

Simulated synapse loss induces depression-like behaviors in deep reinforcement learning

Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network—simulating the effect of dendritic spine loss as observed in major depressive disorder (MDD). Surprisingly, this simulated spine loss is sufficient to induce a variety of MDD-like behaviors in the artificially intelligent agent, including anhedonia, increased temporal discounting, avoidance, and an altered exploration/exploitation balance. Furthermore, simulating alternative and longstanding reward-processing-centric conceptions of MDD (dysfunction of the dopamine system, altered reward discounting, context-dependent learning rates, increased exploration) does not produce the same range of MDD-like behaviors. These results support a conceptual model of MDD as a reduction of brain connectivity (and thus information-processing capacity) rather than an imbalance in monoamines—though the computational model suggests a possible explanation for the dysfunction of dopamine systems in MDD. Reversing the spine-loss effect in our computational MDD model can lead to rescue of rewarding behavior under some conditions. This supports the search for treatments that increase plasticity and synaptogenesis, and the model suggests some implications for their effective administration.

Molecular Lineages and Spatial Distributions of Subplate Neurons in the Human Fetal Cerebral Cortex.

The expansion of neural progenitors and production of distinct neurons are crucial for architectural assembly and formation of connectivity in human brains. Subplate neurons (SPNs) are among the firstborn neurons in the human fetal cerebral cortex, and play a critical role in establishing intra- and extracortical connections. However, little is known about SPN origin and developmental lineages. In this study, spatial landscapes and molecular trajectories of SPNs in the human fetal cortices from gestational weeks (GW) 10 to 25 are created by performing spatial transcriptomics and single-cell RNA sequencing. Genes known to be evolutionarily human-specific and genes associated with extracellular matrices (ECMs) are found to maintain stable proportions of subplate neurons among other neuronal types. Enriched ECM gene expression in SPNs varies in distinct cortical regions, with the highest level in the frontal lobe of human fetal brains. This study reveals molecular origin and lineage specification of subplate neurons in the human fetal cerebral cortices, and highlights underpinnings of SPNs to cortical neurogenesis and early structural folding.

Asymmetry of attentive networks contributes to adult Attention-deficit/hyperactivity disorder (ADHD) pathophysiology.

Diffusion imaging studies in Attention-deficit/hyperactivity disorder (ADHD) have revealed alterations in anatomical brain connections, such as the fronto-parietal connection known as superior longitudinal fasciculus (SLF). Studies in neurotypical adults have shown that the three SLF branches (SLF I, II, III) support distinct brain functions, such as attention and inhibition; and that their pattern of lateralization is associated with attention performance. However, most studies in ADHD have investigated the SLF as a single bundle and in children; thus, the potential contribution of the lateralization of the SLF branches to adult ADHD pathophysiology remains to be elucidated. We used diffusion-weighted spherical deconvolution tractography to dissect the SLF branches in 60 adults with ADHD (including 26 responders and 34 non-responders to methylphenidate, MPH) and 20 controls. Volume and hindrance modulated orientational anisotropy (HMOA), which respectively reflect white matter macro- and microstructure, were extracted to calculate the corresponding lateralization indices. We tested whether neurotypical controls differed from adults with ADHD, and from treatment response groups in sensitivity analyses; and investigated associations with clinico-neuropsychological profiles. All the three SLF branches were lateralized in adults with ADHD, but not in controls. The lateralization of theSLFI HMOA was associated with performance at the line bisection, not that of the SLF II volume as previously reported in controls. Further, an increased left-lateralization of the SLF I HMOA was associated with higher hyperactivity levels in the ADHD group. Thus, an altered asymmetry of the SLF, perhaps especially of the dorsal branch, may contribute to adult ADHD pathophysiology.

Subcortical Alterations in Newly Diagnosed Epilepsy and Associated Changes in Brain Connectivity and Cognition.

Patients with chronic focal epilepsy commonly exhibit subcortical atrophy, particularly of the thalamus. The timing of these alterations remains uncertain, though preliminary evidence suggests that observable changes may already be present at diagnosis. It is also not yet known how these morphological changes are linked to the coherence of white matter pathways throughout the brain, or to neuropsychological function often compromised before antiseizure medication treatment. This study investigates localized atrophy in subcortical regions using surface shape analysis in individuals with newly diagnosed focal epilepsy (NDfE) and assesses their implications on brain connectivity and cognitive function. We collected structural (T1w) and diffusion-weighted MRI and neuropsychological data from 104 patients with NDfE and 45 healthy controls (HCs) matched for age, sex, and education. A vertex-based shape analysis was performed on subcortical structures to compare patients with NDfE and HC, adjusting for age, sex, and intracranial volume. The mean deformation of significance areas (pcor < 0.05) was used to identify white matter pathways associated with overall shape alterations in patients relative to controls using correlational tractography. Additionally, the relationship between significant subcortical shape values and neuropsychological outcomes was evaluated using a generalized canonical correlation approach. Shape analysis revealed bilateral focal inward deformation (a proxy for localized atrophy) in anterior areas of the right and left thalamus and right pallidum in patients with NDfE compared to HC (FWE corrected). No structures showed areas of outward deformation in patients. The connectometry analysis revealed that fractional anisotropy (FA) was positively correlated with thalamic and pallidal shape deformation, that is, reduced FA was associated with inward deformation in tracts proximal to and or connecting with the thalamus including the fornix, frontal, parahippocampal, and corticothalamic pathways. Thalamic and pallidal shape changes were also related to increased depression and anxiety and reduced memory and cognitive function. These findings suggest that atrophy of the thalamus, which has previously been associated with the generation and maintenance of focal seizures, may present at epilepsy diagnosis and relate to alterations in both white matter connectivity and cognitive performance. We suggest that at least some alterations in brain structure and consequent impact on cognitive and affective processes are the result of early epileptogenic processes rather than exclusively due to the chronicity of longstanding epilepsy, recurrent seizures, and treatment with antiseizure medication.

Resting-State Functional Magnetic Resonance Imaging Reveals the Effects of rTMS on Neural Activity and Brain Connectivity After Experimental Stroke.

Limited understanding of neurobiological mechanisms of repetitive transcranial magnetic stimulation (rTMS) prevents us from choosing optimal therapeutic regimen for patients to improve therapeutic efficiency. Resting-state functional magnetic resonance imaging (rs-fMRI) has been demonstrated to obtain comparable functional readouts across species. Intermittent and continuous theta burst stimulation were used to stimulate ipsilesional and contralesional hemisphere, respectively, during the subacute phase after stroke. We used a rat middle cerebral artery occlusion stroke model. The amplitude of low-frequency fluctuations and functional connectivity analyses of rs-fMRI were chosen to detect neuron activity and functional connectivity. The expression of neuron activation marker c-Fos and axonal plasticity marker GAP43 was examined by an immunochemistry method to corroborate the results of rs-fMRI. iTBS altered the long-term neuronal activity in bilateral sensorimotor cortex, whereas cTBS influenced immediate neuronal activity of bilateral sensorimotor cortex. In addition, cTBS enhanced interhemispheric and intrahemisheric functional connectivity in contralesional hemisphere, accompanied by axonal and dendritic remodeling in the perilesional cortical areas and contralesional homologous areas after large stroke. rTMS exerted complex effects on brain structural and functional connectivity in addition to affecting cortical excitability. cTBS promoted the compensatory effect of contralesional hemisphere after stroke with large lesions.

Working memory related functional connectivity in adult ADHD and its amenability to training: A randomized controlled trial

BackgroundWorking memory (WM) deficits are among the most prominent cognitive impairments in attention deficit hyperactivity disorder (ADHD). While functional connectivity is a prevailing approach in brain imaging of ADHD, alterations in WM-related functional brain networks and their malleability by cognitive training are not well known. We examined whole-brain functional connectivity differences between adults with and without ADHD during n-back WM tasks and rest at pretest, as well as the effects of WM training on functional and structural brain connectivity in the ADHD group. MethodsForty-two adults with ADHD and 36 neurotypical controls performed visuospatial and verbal n-back tasks during functional magnetic resonance imaging (fMRI). In addition, seven-minute resting state fMRI data and diffusion-weighted MR images were collected from all participants. The adults with ADHD continued into a 5-week randomized controlled WM training trial (experimental group training on a dual n-back task, n = 21; active control group training on Bejeweled II video game, n = 21), followed by a posttraining MRI. Brain connectivity was examined with Network-Based Statistic. ResultsAt the pretest, adults with ADHD had decreased functional connectivity compared with the neurotypical controls during both n-back tasks in networks encompassing fronto-parietal, temporal, occipital, cerebellar, and subcortical brain regions. Furthermore, WM-related connectivity in widespread networks was associated with performance accuracy in a continuous performance test. Regarding resting state connectivity, no group differences or associations with task performance were observed. WM training did not modulate functional or structural connectivity compared with the active controls. ConclusionOur results indicate large-scale abnormalities in functional brain networks underlying deficits in verbal and visuospatial WM commonly faced in ADHD. Training-induced plasticity in these networks may be limited