Contrastive voxel clustering for multiscale modeling of brain network.

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.

Neural response during prefrontal theta burst stimulation: Interleaved TMS-fMRI of full iTBS protocols.

BACKGROUND: Left prefrontal intermittent theta-burst stimulation (iTBS) has emerged as a safe and effective transcranial magnetic stimulation (TMS) treatment protocol in depression. Though network effects after iTBS have been widely studied, the deeper mechanistic understanding of target engagement is still at its beginning. Here, we investigate the feasibility of a novel integrated TMS-fMRI setup and accelerated echo planar imaging protocol to directly observe the immediate effects of full iTBS treatment sessions. OBJECTIVE/HYPOTHESIS: In our effort to explore interleaved iTBS-fMRI feasibility, we hypothesize that TMS will induce acute BOLD signal changes in both the stimulated area and interconnected neural regions. METHODS: Concurrent TMS-fMRI with full sessions of neuronavigated iTBS (i.e. 600 pulses) of the left dorsolateral prefrontal cortex (DLPFC) was investigated in 18 healthy participants. In addition, we conducted four TMS-fMRI sessions in a single patient on long-term maintenance iTBS for bipolar depression to test the transfer to clinical cases. RESULTS: Concurrent TMS-fMRI was feasible for iTBS sequences with 600 pulses. During interleaved iTBS-fMRI, an increase of the BOLD signal was observed in a network including bilateral DLPFC regions. In the clinical case, a reduced BOLD response was found in the left DLPFC and the subgenual anterior cingulate cortex, with high variability across individual sessions. CONCLUSIONS: Full iTBS sessions as applied for the treatment of depressive disorders can be established in the interleaved iTBS-fMRI paradigm. In the future, this experimental approach could be valuable in clinical samples, for demonstrating target engagement by iTBS protocols and investigating their mechanisms of therapeutic action.

Functional MRI of the Brainstem for Assessing Its Autonomic Functions: From Imaging Parameters and Analysis to Functional Atlas.

BACKGROUND: The brainstem is a crucial component of the central autonomic nervous (CAN) system. Functional MRI (fMRI) of the brainstem remains challenging due to a range of factors, including diverse imaging protocols, analysis, and interpretation. PURPOSE: To develop an fMRI protocol for establishing a functional atlas in the brainstem. STUDY TYPE: Prospective cross-sectional study. SUBJECTS: Ten healthy subjects (four males, six females). FIELD STRENGTH/SEQUENCE: Using a 3.0 Tesla MR scanner, we acquired T1-weighted images and three different fMRI scans using fMRI protocols of the optimized functional Imaging of Brainstem (FIBS), the Human Connectome Project (HCP), and the Adolescent Brain Cognitive Development (ABCD) project. ASSESSMENT: The temporal signal-to-noise-ratio (TSNR) of fMRI data was compared between the FIBS, HCP, and ABCD protocols. Additionally, the main normalization algorithms (i.e., FSL-FNIRT, SPM-DARTEL, and ANTS-SyN) were compared to identify the best approach to normalize brainstem data using root-mean-square (RMS) error computed based on manually defined reference points. Finally, a functional autonomic brainstem atlas that maps brainstem regions involved in the CAN system was defined using meta-analysis and data-driven approaches. STATISTICAL TESTS: ANOVA was used to compare the performance of different imaging and preprocessing pipelines with multiple comparison corrections (P ≤ 0.05). Dice coefficient estimated ROI overlap, with 50% overlap between ROIs identified in each approach considered significant. RESULTS: The optimized FIBS protocol showed significantly higher brainstem TSNR than the HCP and ABCD protocols (P ≤ 0.05). Furthermore, FSL-FNIRT RMS error (2.1 ± 1.22 mm; P ≤ 0.001) exceeded SPM (1.5 ± 0.75 mm; P ≤ 0.01) and ANTs (1.1 ± 0.54 mm). Finally, a set of 12 final brainstem ROIs with dice coefficient ≥0.50, as a step toward the development of a functional brainstem atlas. DATA CONCLUSION: The FIBS protocol yielded more robust brainstem CAN results and outperformed both the HCP and ABCD protocols. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

DRD2 TaqIA polymorphism-related functional connectivity between anterior insula and dorsolateral prefrontal cortex predicts the retention time in heroin-dependent individuals under methadone maintenance treatment.

BACKGROUND: Dopamine receptor D2 (DRD2) TaqIA polymorphism has an influence on addiction treatment response and prognosis by mediating brain dopaminergic system efficacy. Insula is crucial for conscious urges to take drugs and maintain drug use. However, it remains unclear about the contribution of DRD2 TaqIA polymorphism to the regulation of insular on addiction behavioral and its relation with the therapeutic effect of methadone maintenance treatment (MMT). METHODS: 57 male former heroin dependents receiving stable MMT and 49 matched male healthy controls (HC) were enrolled. Salivary genotyping for DRD2 TaqA1 and A2 alleles, brain resting-state functional MRI scan and a 24-month follow-up for collecting illegal-drug-use information was conducted and followed by clustering of functional connectivity (FC) patterns of HC insula, insula subregion parcellation of MMT patients, comparing the whole brain FC maps between the A1 carriers and non-carriers and analyzing the correlation between the genotype-related FC of insula sub-regions with the retention time in MMT patients by Cox regression. RESULTS: Two insula subregions were identified: the anterior insula (AI) and the posterior insula (PI) subregion. The A1 carriers had a reduced FC between the left AI and the right dorsolateral prefrontal cortex (dlPFC) relative to no carriers. And this reduced FC was a poor prognostic factor for the retention time in MMT patients. CONCLUSION: DRD2 TaqIA polymorphism affects the retention time in heroin-dependent individuals under MMT by mediating the functional connectivity strength between left AI and right dlPFC, and the two brain regions are promising therapeutic targets for individualized treatment.

Changes in working memory brain activity and task-based connectivity after long-duration spaceflight.

We studied the longitudinal effects of approximately 6 months of spaceflight on brain activity and task-based connectivity during a spatial working memory (SWM) task. We further investigated whether any brain changes correlated with changes in SWM performance from pre- to post-flight. Brain activity was measured using functional magnetic resonance imaging while astronauts (n = 15) performed a SWM task. Data were collected twice pre-flight and 4 times post-flight. No significant effects on SWM performance or brain activity were found due to spaceflight; however, significant pre- to post-flight changes in brain connectivity were evident. Superior occipital gyrus showed pre- to post-flight reductions in task-based connectivity with the rest of the brain. There was also decreased connectivity between the left middle occipital gyrus and the left parahippocampal gyrus, left cerebellum, and left lateral occipital cortex during SWM performance. These results may reflect increased visual network modularity with spaceflight. Further, increased visual and visuomotor connectivity were correlated with improved SWM performance from pre- to post-flight, while decreased visual and visual-frontal cortical connectivity were associated with poorer performance post-flight. These results suggest that while SWM performance remains consistent from pre- to post-flight, underlying changes in connectivity among supporting networks suggest both disruptive and compensatory alterations due to spaceflight.

Neural correlates of changing food choices while bypassing values.

Current theories suggest that altering choices requires value modification. To investigate this, normal-weight female participants' food choices and values were tested before and after an approach-avoidance training (AAT), while neural activity was recorded during the choice task using functional magnetic resonance imaging (fMRI). During AAT, participants consistently approached low- while avoiding high-calorie food cues. AAT facilitated low-calorie food choices, leaving food values unchanged. Instead, we observed a shift in indifference points, indicating the decreased contribution of food values in food choices. Training-induced choice shifts were associated with increased activity in the posterior cingulate cortex (PCC). In contrast, the medial PFC activity was not changed. Additionally, PCC gray matter density predicted individual differences in training-induced functional changes, suggesting anatomic predispositions to training impact. Our findings demonstrate neural mechanisms underlying choice modulation independent of valuation-related processes, which has substantial theoretical significance for decision-making frameworks and translational implications for health-related decisions resilient to value shifts.

Hazardous or harmful alcohol use and reward processing in people with HIV.

The intersecting epidemics of HIV and hazardous or harmful alcohol use (HAU) can have significant detrimental consequences. Both HIV and HAU have independent negative influences on executive function. Dysfunction in reward processing may play a role in these co-occurring epidemics. In this cross-sectional case-control study, we investigated the association of HAU with reward processing amongst people with HIV (PWH). We investigated the function of the ventral-striatal reward system using a functional MRI (fMRI) monetary incentive delay (MID) task in a sample of 60 South African adults (mean age 32.7 years): 42 living with HIV and on ART (21 with harmful alcohol use [HIV + HAU], 21 without [HIV-HAU]) and 18 healthy controls, matched for age, gender, and resident community. Education significantly influenced task performance, with those with a secondary level of education demonstrating a greater increase in reaction time (p = 0.048) and accuracy (p = 0.002) than those without. There were no significant differences in reward anticipation in the ventral striatum (VS) between HIV + HAU, HIV-HAU, and healthy controls when controlling for level of education. There were also no significant differences in reward outcome in the orbitofrontal cortex (OFC) between HIV + HAU, HIV-HAU, and healthy controls when controlling for level of education. In a sample of South African adults, we did not demonstrate significant differences in reward anticipation in the VS and reward outcome in the OFC in PWH, with and without HAU, and controls. Factors, such as task performance, education, and depression may have influenced our results. Further studies are needed to better delineate the potential links between HIV, HAU, and depression and reward system function.

Multi-level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study.

BACKGROUND: While identifying risk factors for adolescent depression is critical for early prevention and intervention, most studies have sought to understand the role of isolated factors rather than across a broad set of factors. Here, we sought to examine multi-level factors that maximize the prediction of depression symptoms in US children participating in the Adolescent Brain and Cognitive Development (ABCD) study. METHODS: A total of 7,995 participants from ABCD (version 3.0 release) provided complete data at baseline and 1-year follow-up data. Depression symptoms were measured with the Child Behavior Checklist. Predictive features included child demographic, environmental, and structural and resting-state fMRI variables, parental depression history and demographic characteristics. We used linear (elastic net regression, EN) and non-linear (gradient-boosted trees, GBT) predictive models to identify which set of features maximized prediction of depression symptoms at baseline and, separately, at 1-year follow-up. RESULTS: Both linear and non-linear models achieved comparable results for predicting baseline (EN: MAE = 3.757; R2 = 0.156; GBT: MAE = 3.761; R2 = 0.147) and 1-year follow-up (EN: MAE = 4.255; R2 = 0.103; GBT: MAE = 4.262; R2 = 0.089) depression. Parental history of depression, greater family conflict, and shorter child sleep duration were among the top predictors of concurrent and future child depression symptoms across both models. Although resting-state fMRI features were relatively weaker predictors, functional connectivity of the caudate was consistently the strongest neural feature associated with depression symptoms at both timepoints. CONCLUSIONS: Consistent with prior research, parental mental health, family environment, and child sleep quality are important risk factors for youth depression. Functional connectivity of the caudate is a relatively weaker predictor of depression symptoms but may represent a biomarker for depression risk.

Reward- and threat-related neural function associated with risk and presence of depression in adolescents: a study using a composite risk score in Brazil.

BACKGROUND: Neuroimaging studies on adolescents at risk for depression have relied on a single risk factor and focused on adolescents in high-income countries. Using a composite risk score, this study aims to examine neural activity and connectivity associated with risk and presence of depression in adolescents in Brazil. METHODS: Depression risk was defined with the Identifying Depression Early in Adolescence Risk Score (IDEA-RS), calculated using a prognostic model that included 11 socio-demographic risk factors. Adolescents recruited from schools in Porto Alegre were classified into a low-risk (i.e., low IDEA-RS and no lifetime depression), high-risk (i.e., high IDEA-RS and no lifetime depression), or clinically depressed group (i.e., high IDEA-RS and depression diagnosis). One hundred fifty adolescents underwent a functional MRI scan while completing a reward-related gambling and a threat-related face-matching task. We compared group differences in activity and connectivity of the ventral striatum (VS) and amygdala during the gambling and face-matching tasks, respectively, and group differences in whole-brain neural activity. RESULTS: Although there was no group difference in reward-related VS or threat-related amygdala activity, the depressed group showed elevated VS activity to punishment relative to high-risk adolescents. The whole-brain analysis found reduced reward-related activity in the lateral prefrontal cortex of patients and high-risk adolescents compared with low-risk adolescents. Compared with low-risk adolescents, high-risk and depressed adolescents showed reduced threat-related left amygdala connectivity with thalamus, superior temporal gyrus, inferior parietal gyrus, precentral gyrus, and supplementary motor area. CONCLUSIONS: We identified neural correlates associated with risk and presence of depression in a well-characterized sample of adolescents. These findings enhance knowledge of the neurobiological underpinnings of risk and presence of depression in Brazil. Future longitudinal studies are needed to examine whether the observed neural patterns of high-risk adolescents predict the development of depression.

Causal effects of psychostimulants on neural connectivity: a mechanistic, randomized clinical trial.

BACKGROUND: Psychostimulants are frequently used to treat attention-deficit/hyperactivity disorder (ADHD), but side effects are common leading to many patients discontinuing treatment. Identifying neural mechanisms by which psychostimulants attenuate symptoms may guide the development of more refined and tolerable therapeutics. METHODS: We conducted a 12-week, randomized, placebo-controlled trial (RCT) of a long-acting amphetamine, lisdexamfetamine (LDEX), in patients with ADHD, ages 6-25 years old. Of the 58 participants who participated in the RCT, 49 completed pre- and post-RCT magnetic resonance imaging scanning with adequate data quality. Healthy controls (HCs; n = 46) were included for comparison. Treatment effects on striatal and thalamic functional connectivity (FC) were identified using static (time-averaged) and dynamic (time-varying) measures and then correlated with symptom improvement. Analyses were repeated in independent samples from the Adolescent Brain Cognitive Development study (n = 103) and the ADHD-200 Consortium (n = 213). RESULTS: In 49 participants (25 LDEX; 24 Placebo), LDEX increased static and decreased dynamic FC (DFC). However, only DFC was associated with the therapeutic effects of LDEX. Additionally, at baseline, DFC was elevated in unmedicated-ADHD participants relative to HCs. Independent samples yielded similar findings - ADHD was associated with increased DFC, and psychostimulants with reduced DFC. Static FC findings were inconsistent across samples. CONCLUSIONS: Changes in dynamic, but not static, FC were associated with the therapeutic effects of psychostimulants. While prior research has focused on static FC, DFC may offer a more reliable target for new ADHD interventions aimed at stabilizing network dynamics, though this needs confirmation with subsequent investigations.

Development of Individual Variability in Brain Functional Connectivity and Capability across the Adult Lifespan.

Individual variability exists in both brain function and behavioral performance. However, changes in individual variability in brain functional connectivity and capability across adult development and aging have not yet been clearly examined. Based on resting-state functional magnetic resonance imaging data from a large cohort of participants (543 adults, aged 18-88 years), brain functional connectivity was analyzed to characterize the spatial distribution and differences in individual variability across the adult lifespan. Results showed high individual variability in the association cortex over the adult lifespan, whereas individual variability in the primary cortex was comparably lower in the initial stage but increased with age. Individual variability was also negatively correlated with the strength/number of short-, medium-, and long-range functional connections in the brain, with long-range connections playing a more critical role in increasing global individual variability in the aging brain. More importantly, in regard to specific brain regions, individual variability in the motor cortex was significantly correlated with differences in motor capability. Overall, we identified specific patterns of individual variability in brain functional structure during the adult lifespan and demonstrated that functional variability in the brain can reflect behavioral performance. These findings advance our understanding of the underlying principles of the aging brain across the adult lifespan and suggest how to characterize degenerating behavioral capability using imaging biomarkers.

Brain activity during facial processing in autism spectrum disorder: an activation likelihood estimation (ALE) meta-analysis of neuroimaging studies.

BACKGROUND: Though aberrant face processing is a hallmark of autistic spectrum disorder (ASD), findings on accompanying brain activity are divergent. Therefore, we conducted an activation likelihood estimation (ALE) meta-analysis of studies examining brain activity during face processing. METHODS: We searched PubMed and PsycINFO using combinations of terms as 'fMRI', 'Autism Spectrum Disorder', 'Face Perception'. Eligible studies reported on DSM-diagnosed ASD individuals, compared to controls (HC), using face stimuli presented in fMRI and reporting whole-brain analysis coordinates. We compared two approaches: 'convergence of differences' (primary analysis) using study-level coordinates from ASD vs. HC contrasts, and 'differences in convergence' (secondary) pooling coordinates within each group separately, and contrasting the resultant ALE maps. RESULTS: Thirty-five studies (655 ASD and 668 HC) were included. Primary analysis identified a cluster in amygdala/parahippocampus where HC showed greater convergence of activation. Secondary analysis yielded no significant results. CONCLUSIONS: Results suggest that ASD dysfunction in face processing relies on structures involved in emotional processing rather than perception. We also demonstrate that the two ALE methodologies lead to divergent results.

Disruptions in global network segregation and integration in adolescents and young adults with fetal alcohol spectrum disorder.

BACKGROUND: Fetal alcohol spectrum disorder (FASD) is a significant public health problem that is associated with a broad range of physical, neurocognitive, and behavioral effects resulting from prenatal alcohol exposure (PAE). Magnetic resonance imaging (MRI) has been an important tool for advancing our knowledge of abnormal brain structure and function in individuals with FASD. However, whereas only a small number of studies have applied graph theory-based network analysis to resting-state functional MRI (fMRI) data in individuals with FASD additional research in this area is needed. METHODS: Resting-state fMRI data were collected from adolescent and young adult participants (ages 12-22) with fetal alcohol syndrome (FAS) or alcohol-related neurodevelopmental disorder (ARND) and neurotypically developing controls (CNTRL) from previous studies. Group independent components analysis (gICA) was applied to fMRI data to extract components representing functional brain networks. Functional network connectivity (FNC), measured by Pearson correlation of the average independent component (IC) time series, was analyzed under a graph theory framework to compare network modularity, the average clustering coefficient, characteristic path length, and global efficiency between groups. Cognitive intelligence, measured by the Wechsler Abbreviated Scale of Intelligence (WASI), was compared and correlated to global network measures. RESULTS: Group comparisons revealed significant differences in the average clustering coefficient, characteristic path length, and global efficiency. Modularity was not significantly different between groups. The FAS and ARND groups scored significantly lower than the CNTRL group on Full Scale IQ (FS-IQ) and the Vocabulary subtest, but not the Matrix Reasoning subtest. No significant associations between intelligence and graph theory measures were detected. CONCLUSION: Our results partially agree with previous studies examining global graph theory metrics in children and adolescents with FASD and suggest that the exposure to alcohol during prenatal development leads to disruptions in aspects of functional network segregation and integration.

Ultra-high field (10.5 T) resting state fMRI in the macaque.

Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for contrast agents limiting translatability, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, imaging at magnetic fields above 7T has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed single-subject resting state analysis at high resolutions using a 10.5 Tesla scanner. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI 'macaque connectome' will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.

Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches.

Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these theories can be unified. Two existing recent theories state that 1) functional networks can be explained by all possible walks in the structural network, which we will refer to as the series expansion approach, and 2) functional networks can be explained by a weighted combination of the eigenmodes of the structural network, the so-called eigenmode approach. To elucidate the unique or common explanatory power of these approaches to estimate functional networks from the structural network, we analysed the relationship between these two existing views. Using linear algebra, we first show that the eigenmode approach can be written in terms of the series expansion approach, i.e., walks on the structural network associated with different hop counts correspond to different weightings of the eigenvectors of this network. Second, we provide explicit expressions for the coefficients for both the eigenmode and series expansion approach. These theoretical results were verified by empirical data from Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), demonstrating a strong correlation between the mappings based on both approaches. Third, we analytically and empirically demonstrate that the fit of the eigenmode approach to measured functional data is always at least as good as the fit of the series expansion approach, and that errors in the structural data lead to large errors of the estimated coefficients for the series expansion approach. Therefore, we argue that the eigenmode approach should be preferred over the series expansion approach. Results hold for eigenmodes of the weighted adjacency matrices as well as eigenmodes of the graph Laplacian. â€‹Taken together, these results provide an important step towards unification of existing theories regarding the structure-function relationships in brain networks.

The association between resting-state functional magnetic resonance imaging and aortic pulse-wave velocity in healthy adults.

Resting-state functional magnetic resonance imaging (rs-fMRI) is frequently used to study brain function; but, it is unclear whether BOLD-signal fluctuation amplitude and functional connectivity are associated with vascular factors, and how vascular-health factors are reflected in rs-fMRI metrics in the healthy population. As arterial stiffening is a known age-related cardiovascular risk factor, we investigated the associations between aortic stiffening (as measured using pulse-wave velocity [PWV]) and rs-fMRI metrics. We used cardiac MRI to measure aortic PWV (an established indicator of whole-body vascular stiffness), as well as dual-echo pseudo-continuous arterial-spin labeling to measure BOLD and CBF dynamics simultaneously in a group of generally healthy adults. We found that: (1) higher aortic PWV is associated with lower variance in the resting-state BOLD signal; (2) higher PWV is also associated with lower BOLD-based resting-state functional connectivity; (3) regions showing lower connectivity do not fully overlap with those showing lower BOLD variance with higher PWV; (4) CBF signal variance is a significant mediator of the above findings, only when averaged across regions-of-interest. Furthermore, we found no significant association between BOLD signal variance and systolic blood pressure, which is also a known predictor of vascular stiffness. Age-related vascular stiffness, as measured by PWV, provides a unique scenario to demonstrate the extent of vascular bias in rs-fMRI signal fluctuations and functional connectivity. These findings suggest that a substantial portion of age-related rs-fMRI differences may be driven by vascular effects rather than directly by brain function.

Data-driven identification of subtypes of executive function across typical development, attention deficit hyperactivity disorder, and autism spectrum disorders.

BACKGROUND: Impairment of executive function (EF), the goal-directed regulation of thoughts, actions, and emotions, drives negative outcomes and is common across neurodevelopmental disorders including attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). A primary challenge to its amelioration is heterogeneity in symptom expression within and across disorders. Parsing this heterogeneity is necessary to attain diagnostic precision, a goal of the NIMH Research Domain Criteria Initiative. We aimed to identify transdiagnostic subtypes of EF that span the normal to impaired spectrum and establish their predictive and neurobiological validity. METHODS: Community detection was applied to clinical parent-report measures in 8-14-year-old children with and without ADHD and ASD from two independent cohorts (discovery N = 320; replication N = 692) to identify subgroups with distinct behavioral profiles. Support vector machine (SVM) classification was used to predict subgroup membership of unseen cases. Preliminary neurobiological validation was obtained with existing functional magnetic resonance imaging (fMRI) data on a subsample (N = 84) by testing hypotheses about sensitivity of EF subgroups versus DSM categories. RESULTS: We observed three transdiagnostic EF subtypes characterized by behavioral profiles that were defined by relative weakness in: (a) flexibility and emotion regulation; (b) inhibition; and (c) working memory, organization, and planning. The same tripartite structure was also present in the typically developing children. SVM trained on the discovery sample and tested on the replication sample classified subgroup membership with 77.0% accuracy. Split-half SVM classification on the combined sample (N = 1,012) yielded 88.9% accuracy (this SVM is available for public use). As hypothesized, frontal-parietal engagement was better distinguished by EF subtype than DSM diagnosis and the subgroup characterized with inflexibility failed to modulate right IPL activation in response to increased executive demands. CONCLUSIONS: The observed transdiagnostic subtypes refine current diagnostic nosology and augment clinical decision-making for personalizing treatment of executive dysfunction in children.

Pediatric Functional Neuroimaging: Practical Tips and Pearls.

OBJECTIVE. Functional MRI (fMRI) is clinically used for localization of eloquent cortex before surgical intervention, most commonly motor and language function in patients with tumors or epilepsy. In the pediatric population, special considerations for fMRI relate to limited examination tolerance, small head size, developing anatomy and physiology, and diverse potential abnormalities. In this article, we will highlight pearls and pitfalls of clinical pediatric fMRI including blood oxygenation level-dependent imaging principles, patient preparation, study acquisition, data postprocessing, and examination interpretation. CONCLUSION. Clinical fMRI is indicated for presurgical localization of eloquent cortex in patients with tumors, epilepsy, or other neurologic conditions and requires a solid understanding of technical considerations and data processing. In children, special approaches are needed for patient preparation as well as study design, acquisition, and interpretation. Radiologists should be cognizant of developmental neuroanatomy, causes of neuropathology, and capacity for neuroplasticity in the pediatric population.

Mapping critical hubs of receptive and expressive language using MEG: A comparison against fMRI.

The complexity of the widespread language network makes it challenging for accurate localization and lateralization. Using large-scale connectivity and graph-theoretical analyses of task-based magnetoencephalography (MEG), we aimed to provide robust representations of receptive and expressive language processes, comparable with spatial profiles of corresponding functional magnetic resonance imaging (fMRI). We examined MEG and fMRI data from 12 healthy young adults (age 20-37 years) completing covert auditory word-recognition task (WRT) and covert auditory verb-generation task (VGT). For MEG language mapping, broadband (3-30 Hz) beamformer sources were estimated, voxel-level connectivity was quantified using phase locking value, and highly connected hubs were characterized using eigenvector centrality graph measure. fMRI data were analyzed using a classic general linear model approach. A laterality index (LI) was computed for 20 language-specific frontotemporal regions for both MEG and fMRI. MEG network analysis showed bilateral and symmetrically distributed hubs within the left and right superior temporal gyrus (STG) during WRT and predominant hubs in left inferior prefrontal gyrus (IFG) during VGT. MEG and fMRI localization maps showed high correlation values within frontotemporal regions during WRT and VGT (r = 0.63, 0.74, q < 0.05, respectively). Despite good concordance in localization, notable discordances were observed in lateralization between MEG and fMRI. During WRT, MEG favored a left-hemispheric dominance of left STG (LI = 0.25 ±â€¯0.22) whereas fMRI supported a bilateral representation of STG (LI = 0.08 ±â€¯0.2). Laterality of MEG and fMRI during VGT consistently showed a strong asymmetry in left IFG regions (MEG-LI = 0.45 ±â€¯0.35 and fMRI-LI = 0.46 ±â€¯0.13). Our results demonstrate the utility of a large-scale connectivity and graph theoretical analyses for robust identification of language-specific regions. MEG hubs are in great agreement with the literature in revealing with canonical and extra-canonical language sites, thus providing additional support for the underlying topological organization of receptive and expressive language cortices. Discordances in lateralization may emphasize the need for multimodal integration of MEG and fMRI to obtain an excellent predictive value in a heterogeneous healthy population and patients with neurosurgical conditions.

Physical exercise enhances adult cortical plasticity in a neonatal rat model of hypoxic-ischemic injury: Evidence from BOLD-fMRI and electrophysiological recordings.

Neuroplasticity is considered essential for recovery from brain injury in developing brains. Recent studies indicate that it is especially effective during early postnatal development and during the critical period. The current study used functional magnetic resonance imaging (fMRI) and local field potential (LFP) electrophysiological recordings in rats that experienced neonatal hypoxic-ischemic (HI) injury during the critical period to demonstrate that physical exercise (PE) can improve cortical plasticity even when performed during adulthood, after the critical period. We investigated to what extent the blood oxygen level-dependent (BOLD)-fMRI responses were increased in the contralesional spared cortex, and how these increases were related to the LFP electrophysiological measurements and the functional outcome. The balance of excitation and inhibition was assessed by measuring excitatory and inhibitory postsynaptic currents in stellate cells in the primary somatosensory (S1) cortex, which was compared with the BOLD-fMRI responses in the contralesional S1 cortex. The ratio of inhibitory postsynaptic current (IPSC) to excitatory postsynaptic current (EPSC) at the thalamocortical (TC) input to the spared S1 cortex was significantly increased by PE, which is consistent with the increased BOLD-fMRI responses and improved functional outcome. Our data clearly demonstrate in an experimental rat model of HI injury during the critical period that PE in adulthood enhances neuroplasticity and suggest that enhanced feed-forward inhibition at the TC input to the S1 cortex might underlie the PE-induced amelioration of the somatosensory deficits caused by the HI injury. In summary, the results of the current study indicate that PE, even if performed beyond the critical period or during adulthood, can be an effective therapy to treat neonatal brain injuries, providing a potential mechanism for the development of a potent rehabilitation strategy to alleviate HI-induced neurological impairments.