Functional MRI Data Research Articles

Functional connectivity compensation in Parkinson's disease with freezing of gait.

Freezing of gait (FOG) is a disabling motor symptom prevalent in patients with Parkinson's disease (PD); however, its pathophysiological mechanisms are poorly understood. This study aimed to investigate whole-brain functional connectivity (FC) pattern alterations in PD patients with FOG. A total of 18 PD patients, 10 with FOG (PD-FOG) and 8 without FOG (PD-nFOG), and 10 healthy controls were enrolled. High-resolution 3D T1-weighted and resting-state functional MRI (rs-fMRI) data were obtained from all participants. The groups' internetwork connectivity differences were explored with rs-fMRI FC using seed-based analysis and graph theory. Multiple linear regression analysis estimated the relationship between FC changes and clinical measurements. Rs-fMRI analysis demonstrated alterations in FC in various brain regions between the three groups. Freezing of Gait Questionnaire severity was correlated with decreased brain functional connection between Vermis12 and the left temporal occipital fusiform cortex (r = -0.82, P < .001). Graph theory topological metrics indicated a decreased clustering coefficient in the right superior temporal gyrus in the PD-nFOG group. PD-FOG patients exhibited a compensatory increase in connectivity between the left inferior frontal gyrus language network and the postcentral gyrus compared to PD-nFOG patients. Further, the decreased connection between Vermis 12 and the left temporal occipital fusiform cortex may serve as a potential neuroimaging biomarker for tracking PD-FOG and distinguishing between PD subtypes.

Enhancing chronic low back pain management: an initial neuroimaging study of a mobile interoceptive attention training.

Chronic low back pain (cLBP) poses significant challenges, often addressed through avoidance or distraction. Emerging evidence suggests that mind-body interventions, like our novel Mind Your Pain (MyP) smartphone mobile application, may offer relief. We conducted a single-arm, mixed-methods neuroimaging study to assess the degree to which treatment response to our 8-week intervention, as measured by the reduction in the Pain, Enjoyment of Life and General Activity Scale (PEG), was associated with enhanced pain-related insula activation over time. Twenty-nine individuals with cLBP completed patient-reported assessments, qualitative sensory testing (QST) measures, and neuroimaging pre- and post-MyP. Functional MRI data during experimental heat pain on the left forearm were collected and analyzed, comparing responders (≥50% reduction in PEG scores) and non-responders. MyP led to significant decreases in PEG scores overall. Furthermore, MyP responders exhibited increased pain-related activation in key brain regions, including the contralateral posterior insula, bilateral ventral anterior insula, ventral anterior cingulate, dorsolateral prefrontal cortex, and nucleus accumbens. Although baseline behavioral and sensory measures did not differ between the two responder groups, baseline neural differences related to the impact of the endogenous back pain were observed. MyP appears to modify pain response and underlying neural circuitry, suggesting neural changes in interoception may serve as biomarkers for mind-body interventions in cLBP. This study highlights the potential of MyP as a novel approach for cLBP management, warranting further investigation.

Limited cerebellar gradient extension in temporal lobe epilepsy with dystonic posturing.

Dystonic posturing (DP) is a common semiology in temporal lobe epilepsy (TLE). We aimed to explore cerebellar gradient alterations in functional connectivity in TLE patients with and without DP. Resting-state functional MRI data were obtained in 60 TLE patients and 32 matched healthy controls. Patients were further divided into two groups: TLE with DP (TLE + DP, 31 patients) and TLE without DP (TLP-DP, 29 patients). We explored functional gradient alterations in the cerebellum based on cerebellar-cerebral functional connectivity and combined with independent component analysis to evaluate cerebellar-cerebral functional integration and reveal the contribution of the motor components to the gradient. There were no obvious differences in clinical features and postoperative seizure outcomes between TLE + DP and TLE-DP patients. Patients and controls all showed a clear unimodal-to-transmodal gradient transition in the cerebellum, while TLE patients demonstrated an extended principal gradient in functional connectivity compared to healthy controls, which was more limited in TLE + DP patients. Gradient alterations were more widespread in TLE-DP patients, involving bilateral cerebellum, while gradient alterations in TLE + DP patients were limited in the cerebellum ipsilateral to the seizure focus. In addition, more cerebellar motor components contributed to the gradient alterations in TLE + DP patients, mainly in ipsilateral cerebellum. Extended cerebellar principal gradients in functional connectivity revealed excessive functional segregation between unimodal and transmodal systems in TLE. The functional connectivity gradients were more limited in TLE + DP patients. Functional connectivity in TLE patients with dystonic posturing involved more contribution of cerebellar motor function to ipsilateral cerebellar gradient. Dystonic posturing contralateral to epileptic focus is a common symptom in temporal lobe epilepsy, and the cerebellum may be involved in its generation. In this study, we found cerebellar gradients alterations in functional connectivity in temporal lobe epilepsy patients with and without contralateral dystonic posturing. In particular, we found that TLE patients with dystonic posturing may have more limited cerebellar gradient in functional connectivity, involving more contribution of cerebellar motor function to ipsilateral cerebellar gradient. Our study suggests a close relationship between ipsilateral cerebellum and contralateral dystonic posturing.

FunMaps: a method for parcellating functional brain networks using resting-state functional MRI data.

Parcellations of resting-state functional magnetic resonance imaging (rs-fMRI) data are widely used to create topographical maps of functional networks in the human brain. While such network maps are highly useful for studying brain organization and function, they usually require large sample sizes to make them, thus creating practical limitations for researchers that would like to carry out parcellations on data collected in their labs. Furthermore, it can be difficult to quantitatively evaluate the results of a parcellation since networks are usually identified using a clustering algorithm, like principal components analysis, on the results of a single group-averaged connectivity map. To address these challenges, we developed the FunMaps method: a parcellation routine that intrinsically incorporates stability and replicability of the parcellation by keeping only network distinctions that agree across halves of the data over multiple random iterations. Here, we demonstrate the efficacy and flexibility of FunMaps, while describing step-by-step instructions for running the program. The FunMaps method is publicly available on GitHub (https://github.com/persichetti-lab/FunMaps). It includes source code for running the parcellation and auxiliary code for preparing data, evaluating the parcellation, and displaying the results.

Role of dorsal striatum circuits in relapse to opioid seeking after voluntary abstinence

AbstractHigh relapse rate during abstinence is a defining characteristic of drug addiction. We previously found that opioid seeking progressively increases after voluntary abstinence induced by adverse consequences of oxycodone seeking (crossing an electric barrier). Functional MRI revealed that this effect is associated with changes in functional connectivity within medial orbitofrontal cortex (mOFC)- and dorsomedial striatum (DMS)-related circuits. Here, we used a pharmacological manipulation and fMRI to determine the causal role of mOFC and DMS in oxycodone seeking after electric barrier-induced abstinence. We trained rats to self-administer oxycodone (6 h/day, 14 days). Next, we induced voluntary abstinence by exposing them to an electric barrier for 2 weeks. We inactivated the mOFC and DMS with muscimol+baclofen (GABAa and GABAb receptor agonists) and then tested them for relapse to oxycodone seeking on abstinence days 1 or 15 without the electric barrier or oxycodone. Inactivation of DMS (p &lt; 0.001) but not mOFC decreased oxycodone seeking before or after electric barrier-induced abstinence. Functional MRI data revealed that DMS inactivation decreased cerebral blood volume levels in DMS and several distant cortical and subcortical regions (corrected p &lt; 0.05). Furthermore, functional connectivity of DMS with several frontal, sensorimotor, and auditory regions significantly increased after DMS inactivation (corrected p &lt; 0.05). Finally, an exploratory analysis of an existing functional MRI dataset showed that DMS inactivation restored voluntary abstinence-induced longitudinal changes in DMS functional connectivity with these brain regions (p &lt; 0.05). Results indicate a role of DMS and related brain circuits in oxycodone seeking after voluntary abstinence, suggesting potential targets for intervention.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development.

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

Stimulus repetition induces a two-stage learning process in primary visual cortex.

Repeated stimulus exposure alters the brain's response to the stimulus. We investigated the underlying neural mechanisms by recording functional MRI data from human observers passively viewing 120 presentations of two Gabor patches (each Gabor repeating 60 times). We evaluated support for two prominent models of stimulus repetition, the fatigue model and the sharpening model. Our results uncovered a two-stage learning process in the primary visual cortex. In Stage 1, univariate BOLD activation in V1 decreased over the first twelve repetitions of the stimuli, replicating the well-known effect of repetition suppression. Applying MVPA decoding along with a moving window approach, we found that (1) the decoding accuracy between the two Gabors decreased from above-chance level (∼60% to ∼70%) at the beginning of the stage to chance level at the end of the stage (∼50%). This result, together with the accompanying weight map analysis, suggested that the learning dynamics in Stage 1 were consistent with the predictions of the fatigue model. In Stage 2, univariate BOLD activation for the remaining 48 repetitions of the two stimuli exhibited significant fluctuations but no systematic trend. The MVPA decoding accuracy between the two Gabor patches was at chance level initially and became progressively higher as stimulus repetition continued, rising above and staying above chance level starting at the ∼35th repetition. Thus, results from the second stage supported the notion that sustained and prolonged stimulus repetition prompts sharpened representations. Additional analyses addressed (1) whether the neural patterns within each learning stage remained stable and (2) whether new neural patterns were evoked in Stage 2 relative to Stage 1.

Functional MRI Assessment of Brain Activity Patterns Associated with Reading in Medulloblastoma Survivors.

Medulloblastoma, a malignant brain tumor primarily affecting children, poses significant challenges to patients and clinicians due to its complex treatment and potential long-term cognitive consequences. While recent advancements in treatment have significantly improved survival rates, survivors often face cognitive impairments, particularly in reading, impacting their quality of life. According to the double deficit theory, reading impairments are caused by deficits in one or both of two independent reading-related functions: phonological awareness and rapid visual naming. This longitudinal study investigates neurofunctional changes related to reading in medulloblastoma survivors in comparison to controls using functional MRI acquired during rapid automatized naming tasks over three annual visits. Support vector machine classification of functional MRI data reveals a progressive divergence in brain activity patterns between medulloblastoma survivors and healthy controls over time, suggesting delayed effects of cancer treatment on brain function. Alterations in brain regions involved in visual processing and orthographic recognition during rapid naming tasks imply disruptions in the ventral visual pathway associated with normal orthographic processing. These alterations are correlated with performance in tasks involving sound awareness, reading fluency, and word attack. These findings underscore the dynamic nature of post-treatment neurofunctional alterations and the importance of early identification and intervention to address cognitive deficits in survivors.

No evidence of altered language laterality in people who stutter across different brain imaging studies of speech and language.

A long-standing neurobiological explanation of stuttering is the incomplete cerebral dominance theory, which refers to competition between two hemispheres for 'dominance' over handedness and speech, causing altered language lateralization. Renewed interest in these ideas came from brain imaging findings in people who stutter of increased activity in the right hemisphere during speech production or of shifts in activity from right to left when fluency increased. Here, we revisited this theory using functional MRI data from children and adults who stutter, and typically fluent speakers (119 participants in total) during four different speech and language tasks: overt sentence reading, overt picture description, covert sentence reading and covert auditory naming. Laterality indices were calculated for the frontal and temporal lobes using the laterality index toolbox running in Statistical Parametric Mapping. We also repeated the analyses with more specific language regions, namely the pars opercularis (Brodmann area 44) and pars triangularis (Brodmann area 45). Laterality indices in people who stutter and typically fluent speakers did not differ, and Bayesian analyses provided moderate to anecdotal levels of support for the null hypothesis (i.e. no differences in laterality in people who stutter compared with typically fluent speakers). The proportions of the people who stutter and typically fluent speakers who were left lateralized or had atypical rightward or bilateral lateralization did not differ. We found no support for the theory that language laterality is reduced or differs in people who stutter compared with typically fluent speakers.

Brain network changes after the first seizure: an insight into medication response?

After a first epileptic seizure, anti-seizure medications (ASMs) can change the likelihood of having a further event. This prospective study aimed to quantify brain network changes associated with taking ASM monotherapy. We applied graph theoretical network analysis to longitudinal resting-state functional MRI (fMRI) data from 28 participants who had recently experienced their first seizure. Participants were imaged before and during long-term ASM therapy, with a mean inter-scan interval of 6.9 months. After commencing ASM, we observed an increase in the clustering coefficient and a decrease in network path length. Brain changes after ASM treatment were most prominent in the superior frontoparietal and inferior fronto-temporal regions. Participants with recurrent seizures display the most pronounced network changes after ASM treatment. This study shows changes in brain network function after ASM administration, particularly in participants with recurrent seizures. Larger studies that ideally include control cohorts are required to understand further the connection between ASM-related brain network changes and longer-term seizure status.

A Plug-In Graph Neural Network to Boost Temporal Sensitivity in fMRI Analysis.

Learning-based methods offer performance leaps over traditional methods in classification analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning models that analyze functional connectivity (FC) features among brain regions have been particularly promising. However, many existing models receive as input temporally static FC features that summarize inter-regional interactions across an entire scan, reducing the temporal sensitivity of classifiers by limiting their ability to leverage information on dynamic FC features of brain activity. To improve the performance of baseline classification models without compromising efficiency, here we propose a novel plug-in based on a graph neural network, GraphCorr, to provide enhanced input features to baseline models. The proposed plug-in computes a set of latent FC features with enhanced temporal information while maintaining comparable dimensionality to static features. Taking brain regions as nodes and blood-oxygen-level-dependent (BOLD) signals as node inputs, GraphCorr leverages a node embedder module based on a transformer encoder to capture dynamic latent representations of BOLD signals. GraphCorr also leverages a lag filter module to account for delayed interactions across nodes by learning correlational features of windowed BOLD signals across time delays. These two feature groups are then fused via a message passing algorithm executed on the formulated graph. Comprehensive demonstrations on three public datasets indicate improved classification performance for several state-of-the-art graph and convolutional baseline models when they are augmented with GraphCorr.

Dynamic Functional Network Connectivity in Acute Incomplete Cervical Cord Injury Patients and Its Associations With Sensorimotor Dysfunction Measures

BackgroundDynamic functional network connectivity (dFNC) captures temporal variations in functional connectivity during MRI acquisition. However, the neural mechanisms driving dFNC alterations in the brain networks of patients with Acute incomplete cervical cord injury (AICCI) remain unclear. MethodsThis study included 16 AICCI patients and 16 healthy controls (HC). Initially, Independent Component Analysis (ICA) was employed to extract whole-brain independent components (ICs) from resting-state functional MRI (rs-fMRI) data. Subsequently, a sliding time window approach, combined with k-means clustering, was used to estimate dFNC states for each participant. Finally, a correlation analysis was conducted to examine the association between sensorimotor dysfunction scores in AICCI patients and the temporal characteristics of dFNC. ResultICA was employed to extract 26 whole-brain ICs. Subsequent dynamic analysis identified four distinct connectivity states across the entire cohort. Notably, AICCI patients demonstrated a significant preference for State 3 compared to HC, as evidenced by a higher frequency and longer duration spent in this state. Conversely, State 4 exhibited a reduced frequency and shorter dwell time in AICCI patients. Moreover, correlation analysis revealed a positive association between sensorimotor dysfunction and both the mean dwell time and the fractional of time spent in State 3. ConclusionsPatients with AICCI demonstrate abnormal connectivity within dFNC states, and the temporal characteristics of dFNC are associated with sensorimotor dysfunction scores. These findings highlight the potential of dFNC as a sensitive biomarker for detecting network functional changes in AICCI patients, providing valuable insights into the dynamic alterations in brain connectivity related to sensorimotor dysfunction in this population.

Resting-State Functional Connectivity of the Amygdala in Autism: A Preregistered Large-Scale Study.

Three leading neurobiological hypotheses about autism spectrum disorder (ASD) propose underconnectivity between brain regions, atypical function of the amygdala, and generally higher variability between individuals with ASD than between neurotypical individuals. Past work has often failed to generalize, because of small sample sizes, unquantified data quality, and analytic flexibility. This study addressed these limitations while testing the above three hypotheses, applied to amygdala functional connectivity. In a comprehensive preregistered study, the three hypotheses were tested in a subset (N=488 after exclusions; N=212 with ASD) of the Autism Brain Imaging Data Exchange data sets. The authors analyzed resting-state functional connectivity (FC) from functional MRI data from two anatomically defined amygdala subdivisions, in three hypotheses with respect to magnitude, pattern similarity, and variability, across different anatomical scales ranging from whole brain to specific regions and networks. A Bayesian approach to hypothesis evaluation produced inconsistent evidence in ASD for atypical amygdala FC magnitude, strong evidence that the multivariate pattern of FC was typical, and no consistent evidence of increased interindividual variability in FC. The results strongly depended on analytic choices, including preprocessing pipeline for the neuroimaging data, anatomical specificity, and subject exclusions. A preregistered set of analyses found no reliable evidence for atypical functional connectivity of the amygdala in autism, contrary to leading hypotheses. Future studies should test an expanded set of hypotheses across multiple processing pipelines, collect deeper data per individual, and include a greater diversity of participants to ensure robust generalizability of findings on amygdala FC in ASD.

Hippocampal temporal dynamics and spatial heterogeneity unveil vulnerability markers in the offspring of bipolar patients.

Bipolar disorder (BD) is a highly heritable disorder characterized by emotion dysregulation and recurrent oscillations between mood states. Despite the proven efficacy of early interventions, vulnerability markers in high-risk individuals are still lacking. BD patients present structural alterations of the hippocampus, a pivotal hub of emotion regulation networks composed of multiple subregions with different projections. However, the hippocampal dynamic functional connectivity (dFC) in BD remains unclear. We aim to investigate whether the dFC of hippocampal subdivisions differentiates BD patients, offspring of BD patients (BDoff), and healthy controls (HC); and whether it correlates with symptoms differently between these groups. We studied for the first time the dFC of the hippocampus through a cutting-edge micro-co-activation patterns (μCAPs) analysis of resting-state functional MRI data of 97 subjects (26 BD, 18 BDoff, 53 HC). μCAPs allow a data-driven differentiation within the seed region. dFC between the hippocampal body and a somatomotor-μCAP was lower both in BD patients (p-valueFDR:0.00015) and in BDoff (p-valueFDR:0.020) than in HC. Inversely, dFC between the hippocampal head and a limbic-μCAP was higher in BD patients than in HC (p-valueFDR: 0.005). Furthermore, the correlations between a frontoparietal-μCAP and both depression and emotion dysregulation symptoms were significantly higher in BD than HC (p-valueFDR <0.02). Overall, we observed alterations of large-scale functional brain networks associated with decreased cognitive control flexibility and disrupted somatomotor, saliency, and emotion processing in BD. Interestingly, BDoff presented an intermediate phenotype between BD and HC, suggesting that dFC of hippocampal subregions might represent a marker of vulnerability to BD.

Abnormal Degree Centrality in Zoster-Associated Pain with or Without Psychiatric Comorbidities: A Resting-State Functional MRI Study.

Zoster-associated pain (ZAP) is frequently concomitant with psychiatric comorbidities. However, the underlying neuropathological mechanisms of ZAP with psychiatric comorbidities remain poorly understood. Rest-stating functional MRI (rs-fMRI) data from 41 ZAP patients without anxiety or depression (noA/D-ZAP), 11 ZAP patients with anxiety or depression (A/D-ZAP) and 29 healthy controls (HCs) were acquired. Degree centrality (DC) based on rs-fMRI was used to explore the node changes in the brain functional network in these subjects. Moreover, correlations and receiver operating characteristic curve analysis were performed. One-way analysis of variance revealed abnormal DC values in the right middle frontal gyrus (MFG) and bilateral precuneus among the three groups. Compared with HCs, A/D-ZAP showed increased DC values in the bilateral pons, while noA/D-ZAP showed increased DC values in the right pons, left brainstem and rectal gyrus and decreased DC values in the right cingulate gyrus and bilateral precuneus. A/D-ZAP showed increased DC values in the left MFG and precentral gyrus (PG) compared with noA/D-ZAP. The DC value of the left pons in A/D-ZAP was positively correlated with the self-rating anxiety scale score. Areas under the curve of DC values in the left PG and MFG for distinguishing A/D-ZAP from the noA/D-ZAP group were 0.907 and 1.000, respectively. This study revealed the node differences in the brain functional network of ZAP patients with or without psychiatric comorbidities. In particular, abnormal DC values of the left MFG and PG may play an important role in the neuropathologic mechanism of the disease.

Interference of default mode on attention networks in adults with attention-deficit/hyperactivity disorder and its association with genetic variants and treatment outcomes.

Altered brain functional connectivity has been proposed as the neurobiological underpinnings of attention-deficit/hyperactivity disorder (ADHD), and the default mode interference hypothesis is one of the most popular neuropsychological models. Here, we explored whether this hypothesis is supported in adults with ADHD and the association with high-risk genetic variants and treatment outcomes. Voxel-based whole-brain connectome analysis was conducted on resting-state functional MRI data from 84 adults with ADHD and 89 healthy controls to identify functional connectivity substrates corresponding to ADHD-related alterations. The candidate genetic variants and 12-week cognitive behavioral therapy data were leveraged from the same population to assess these associations. We detected breakdowns of functional connectivity in the precuneus and left middle temporal gyrus in adults with ADHD, with exact contributions from decreased connectivity within the default mode, dorsal and ventral attention networks, as well as increased connectivity among them with the middle temporal gyrus serving as a crucial 'bridge'. Additionally, significant associations between the altered functional connectivity and genetic variants in both MAOA and MAOB were detected. Treatment restored brain function, with the amelioration of connectivity of the middle temporal gyrus, accompanied by improvements in ADHD core symptoms. These findings support the interference of default mode on attention in adults with ADHD and its association with genetic risk variants and clinical management, providing insights into the underlying pathogenesis of ADHD and potential biomarkers for treatment evaluation.

Structural and functional features characterizing the brains of individuals with higher controllability of motor imagery

Motor imagery is a higher-order cognitive brain function that mentally simulates movements without performing the actual physical one. Although motor imagery has attracted the interest of many researchers, and mental practice utilizing motor imagery has been widely used in sports training and post-stroke rehabilitation, neural bases that determine individual differences in motor imagery ability are not well understood. In this study, using controllability of motor imagery (CMI) test that can objectively evaluate individual ability to manipulate one’s imaginary postures, we examined structural and functional features characterizing the brains of individuals with higher controllability of motor imagery, by analyzing T1-weighted structural MRI data obtained from 89 participants and functional MRI data obtained from 28 of 89 participants. The higher CMI test scorers had larger volume in the bilateral superior frontoparietal white matter regions. The CMI test activated the bilateral dorsal premotor cortex (PMD) and superior parietal lobule (SPL); specifically, the left PMD and/or the right SPL enhanced functional coupling with the visual body, somatosensory, and motor/kinesthetic areas in the higher scorers. Hence, controllability of motor imagery is higher for those who well-develop superior frontoparietal network, and for those whose this network accesses these sensory areas to predict the expected multisensory experiences during motor imagery. This study elucidated for the first time the structural and functional features characterizing the brains of individuals with higher controllability of motor imagery, and advanced understanding of individual differences in motor imagery ability.

Individual Deviation-Based Functional Hypergraph for Identifying Subtypes of Autism Spectrum Disorder.

Heterogeneity has been one of the main barriers to understanding and treatment of autism spectrum disorder (ASD). Previous studies have identified several subtypes of ASD through unsupervised clustering analysis. However, most of them primarily depicted the pairwise similarity between individuals through second-order relationships, relying solely on patient data for their calculation. This leads to an underestimation of the complexity inherent in inter-individual relationships and the diagnostic information provided by typical development (TD). To address this, we utilized an elastic net model to construct an individual deviation-based hypergraph (ID-Hypergraph) based on functional MRI data. We then conducted a novel community detection clustering algorithm to the ID-Hypergraph, with the aim of identifying subtypes of ASD. By applying this framework to the Autism Brain Imaging Data Exchange repository data (discovery: 147/125, ASD/TD; replication: 134/132, ASD/TD), we identified four reproducible ASD subtypes with roughly similar patterns of ALFF between the discovery and replication datasets. Moreover, these subtypes significantly varied in communication domains. In addition, we achieved over 80% accuracy for the classification between these subtypes. Taken together, our study demonstrated the effectiveness of identifying subtypes of ASD through the ID-hypergraph, highlighting its potential in elucidating the heterogeneity of ASD and diagnosing ASD subtypes.

Altered connectivity of central autonomic network: effects of dysautonomia in hereditary transthyretin amyloidosis with polyneuropathy

Background Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN) is a progressive fatal disorder caused by deposition of mutant transthyretin (TTR) amyloids mainly in the nerves and heart. Autonomic dysfunction is a major disabling manifestation, affecting 90% of patients with late-onset ATTRv-PN. The current study aimed to investigate brain functional alterations associated with dysautonomia due to peripheral autonomic nerve degeneration in ATTRv-PN. Methods Resting-state functional MRI data were acquired from 43 ATTRv-PN patients predominantly of A97S (p.A117S) genotype, and the functional connectivity of central autonomic regions was assessed. Results Compared with age-matched healthy controls, the ATTRv-PN patients exhibited (1) reduced functional connectivity of the central autonomic regions such as hypothalamus, amygdala, anterior insula, and middle cingulate cortex with brain areas of the limbic, frontal, and somatosensory systems, and (2) correlations of reduced functional autonomic connectivity with the severity of autonomic dysfunction especially orthostatic intolerance, decreased heart rate variability, and greater clinical disability. Conclusions Our findings provide evidence linking peripheral autonomic dysfunction with altered connectivity in the central autonomic network in ATTRv-PN.