Association of Intrinsic Brain Architecture With Changes in Attentional and Mood Symptoms During Development

A comprehensive systematic review of fMRI studies on brain connectivity in healthy children and adolescents: Current insights and future directions

This systematic review considered evidence of children's and adolescents' typical brain connectivity development studied through resting-state functional magnetic resonance imaging (rs-fMRI). With aim of understanding the state of the art, what has been researched thus far and what remains unknown, this paper reviews 58 studies from 2013 to 2023. Considering the results, rs-fMRI stands out as an appropriate technique for studying language and attention within cognitive domains, and personality traits such as impulsivity and empathy. The most used analyses encompass seed-based, independent component analysis (ICA), the amplitude of the low frequency fluctuations (ALFF), and fractional ALFF (fALFF). The findings highlight key themes, including age-related changes in intrinsic connectivity, sex-specific patterns, and the relevance of the Default Mode Network (DMN). Overall, there is a need for longitudinal approaches to trace the typical developmental trajectory of neural networks from childhood through adolescence with fMRI at rest.

The interplay of depressive symptoms and self-efficacy in adolescents: a network analysis approach

Background

Self-efficacy, a critical psychological construct representing an individual's belief in their ability to control their motivation, behavior, and social environment. In adolescents, self-efficacy plays a crucial role in mental health, particularly concerning depressive symptoms. Despite substantial research, the complex interplay between self-efficacy and depressive symptoms in adolescents remains incompletely understood.

Aims

The aim of this study is to investigate the complex interrelationships between self-efficacy and depressive symptoms in adolescents using psychological network analysis.

Methods

The cross-sectional study involved 3,654 adolescents. Self-efficacy was assessed using the General Self-Efficacy Scale (GSES), and depressive symptoms were measured with the Patient Health Questionnaire-9 (PHQ-9). Network analysis, incorporating the least absolute shrinkage and selection operator (LASSO) technique and centrality analysis, constructed and compared self-efficacy networks between depressive symptoms and healthy control groups.

Results

Of the 3,654 participants, 560 (15.32%) met criteria for moderate to severe depressive symptoms (PHQ-9 scores ≥10). Among those with depressive symptoms, 373 (66.61%) had moderate, 126 (22.50%) had moderate-severe, and 61 (10.89%) had severe symptoms. Bivariate correlation analyses revealed a significant negative correlation between depressive symptoms and self-efficacy (r = -0.41, p < 0.001). The results of the network analysis showed significant differences in self-efficacy networks between adolescents with and without depressive symptoms (global strength: S = 0.25, p < 0.05). Depressed participants showed a network with reduced global strength, suggesting diminished interconnectedness among self-efficacy items. Specific connections within the self-efficacy network were altered in the presence of depressive symptoms. Bridge analysis revealed that effort-based problem-solving (bridge strengths = 0.13) and suicidal ideation (bridge strengths = 0.09) were the key bridge nodes.

Conclusion

Adolescent depressive symptoms significantly impacts the self-efficacy network, resulting in diminished integration of self-efficacy and highlighting the complex interplay between self-efficacy and depressive symptoms. These findings challenge the traditional unidimensional view of self-efficacy and emphasize the need for tailored interventions focusing on unique self-efficacy profiles in adolescents with depressive symptoms.

Neuroimaging for precision medicine in psychiatry

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.

Estimating the total variance explained by whole-brain imaging for zero-inflated outcomes

There is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is often widely distributed across the brain, with individual imaging features exhibiting small effect sizes for predicting neurobehavioral phenotypes. The challenge of capturing the total signal is compounded by the distribution of neurobehavioral data, particularly responses to psychological questionnaires, which often feature zero-inflated, highly skewed outcomes. To close this gap, we have developed a novel Variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes. Our zero-inflated variance (ZIV) estimator estimates the fraction of variance explained (FVE) and the proportion of non-null effects (PNN) from large-scale imaging data. In simulations, ZIV demonstrates superior performance over other linear models. When applied to data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we found that whole-brain imaging features contribute to a larger FVE for externalizing behaviors compared to internalizing behaviors. Moreover, focusing on features contributing to the PNN, ZIV estimator localized key neurocircuitry associated with neurobehavioral traits. To the best of our knowledge, the ZIV estimator is the first specialized method for analyzing zero-inflated neuroimaging data, enhancing future studies on brain-behavior relationships and improving the understanding of neurobehavioral disorders.

Letter-speech sound integration in typical reading development during the first years of formal education

This study investigated the neural basis of letter and speech sound (LS) integration in 53 typical readers (35 girls, all White) during the first 2 years of reading education (ages 7-9). Changes in both sensory (multisensory vs unisensory) and linguistic (congruent vs incongruent) aspects of LS integration were examined. The left superior temporal cortex and bilateral inferior frontal cortex showed increasing activation for multisensory over unisensory LS over time, driven by reduced activation to speech sounds. No changes were noted in the congruency effect. However, at age nine, heightened activation to incongruent over congruent LS pairs were observed, correlating with individual differences in reading development. This suggests that the incongruency effect evolves at varying rates depending on reading skills.

Impaired effective functional connectivity in the social preference of children with autism spectrum disorder

Background

The medial prefrontal cortex (mPFC), amygdala (Amyg), and nucleus accumbens (NAc) have been identified as critical players in the social preference of individuals with ASD. However, the specific pathophysiological mechanisms underlying this role requires further clarification. In the current study, we applied Granger Causality Analysis (GCA) to investigate the neural connectivity of these three brain regions of interest (ROIs) in patients with ASD, aiming to elucidate their associations with clinical features of the disorder.

Methods

Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from the ABIDE II database, which included 37 patients with ASD and 50 typically developing (TD) controls. The mPFC, Amyg, and NAc were defined as ROIs, and the differences in fractional amplitude of low-frequency fluctuations (fALFF) within the ROIs between the ASD and TD groups were computed. Subsequently, we employed GCA to investigate the bidirectional effective connectivity between the ROIs and the rest of the brain. Finally, we explored whether this effective connectivity was associated with the social responsiveness scale (SRS) scores of children with ASD.

Results

The fALFF values in the ROIs were reduced in children with ASD when compared to the TD group. In terms of the efferent connectivity from the ROIs to the whole brain, the ASD group exhibited increased connectivity in the right cingulate gyrus and decreased connectivity in the right superior temporal gyrus. Regarding the afferent connectivity from the whole brain to the ROIs, the ASD group displayed increased connectivity in the right globus pallidus and decreased connectivity in the right cerebellar Crus 1 area and left cingulate gyrus. Additionally, we demonstrated a positive correlation between effective connectivity derived from GCA and SRS scores.

Conclusion

Impairments in social preference ASD children is linked to impaired effective connectivity in brain regions associated with social cognition, emotional responses, social rewards, and social decision-making. This finding further reveals the potential neuropathological mechanisms underlying ASD.

Resting-State Functional Connectivity Change in Frontoparietal and Default Mode Networks After Acute Exercise in Youth

BACKGROUND

A single bout of aerobic exercise can provide acute benefits to cognition and emotion in children. Yet, little is known about how acute exercise may impact children's underlying brain networks' resting-state functional connectivity (rsFC).

OBJECTIVE

Using a data-driven multivariate pattern analysis, we investigated the effects of a single dose of exercise on acute rsFC changes in 9-to-13-year-olds.

METHODS

On separate days in a crossover design, participants (N = 21) completed 20-mins of acute treadmill walking at 65-75% heart rate maximum (exercise condition) and seated reading (control condition), with pre- and post-fMRI scans. Multivariate pattern analysis was used to investigate rsFC change between conditions.

RESULTS

Three clusters in the left lateral prefrontal cortex (lPFC) of the frontoparietal network (FPN) had significantly different rsFC after the exercise condition compared to the control condition. Post-hoc analyses revealed that from before to after acute exercise, activity of these FPN clusters became more correlated with bilateral lPFC and the left basal ganglia. Additionally, the left lPFC became more anti-correlated with the precuneus of the default mode network (DMN). An opposite pattern was observed from before to after seated reading.

CONCLUSIONS

The findings suggest that a single dose of exercise increases connectivity within the FPN, FPN integration with subcortical regions involved in movement and cognition, and segregation of FPN and DMN. Such patterns, often associated with healthier cognitive and emotional control, may underlie the transient mental benefits observed following acute exercise in youth.

The default mode network is associated with changes in internalizing and externalizing problems differently in adolescent boys and girls

Internalizing and externalizing problems that emerge during adolescence differentially increase boys' and girls' risk for developing psychiatric disorders. It is not clear, however, whether there are sex differences in the intrinsic functional architecture of the brain that underlie changes in the severity of internalizing and externalizing problems in adolescents. Using resting-state fMRI data and self-reports of behavioral problems obtained from 128 adolescents (73 females; 9-14 years old) at two timepoints, we conducted multivoxel pattern analysis to identify resting-state functional connectivity markers at baseline that predict changes in the severity of internalizing and externalizing problems in boys and girls 2 years later. We found sex-differentiated involvement of the default mode network in changes in internalizing and externalizing problems. Whereas changes in internalizing problems were associated with the dorsal medial subsystem in boys and with the medial temporal subsystem in girls, changes in externalizing problems were predicted by hyperconnectivity between core nodes of the DMN and frontoparietal network in boys and hypoconnectivity between the DMN and affective networks in girls. Our results suggest that different neural mechanisms predict changes in internalizing and externalizing problems in adolescent boys and girls and offer insights concerning mechanisms that underlie sex differences in the expression of psychopathology in adolescence.

Internalizing Symptoms and Adverse Childhood Experiences Associated With Functional Connectivity in a Middle Childhood Sample

BACKGROUND

Research has found overlapping associations in adults of resting-state functional connectivity (RSFC) to both internalizing disorders (e.g., depression, anxiety) and a history of traumatic events. The present study aimed to extend this previous research to a younger sample by examining RSFC associations with both internalizing symptoms and adverse childhood experiences (ACEs) in middle childhood.

METHODS

We used generalized linear mixed models to examine associations between a priori within- and between-network RSFC with child-reported internalizing symptoms and ACEs using the Adolescent Brain Cognitive Development dataset (N = 10,168, mean age = 9.95 years, SD = 0.627).

RESULTS

We found that internalizing symptoms and ACEs were associated with both multiple overlapping and unique RSFC network patterns. Both ACEs and internalizing symptoms were associated with a reduced anticorrelation between the default mode network and the dorsal attention network. However, internalizing symptoms were uniquely associated with lower within-network default mode network connectivity, while ACEs were uniquely associated with both lower between-network connectivity of the auditory network and cingulo-opercular network, and higher within-network frontoparietal network connectivity.

CONCLUSIONS

The present study points to overlap in the RSFC associations with internalizing symptoms and ACEs, as well as important areas of specificity in RSFC associations. Many of the RSFC associations found have been previously implicated in attentional control functions, including modulation of attention to sensory stimuli. This may have critical importance in understanding internalizing symptoms and outcomes of ACEs.

Cognition and Brain System Segregation in Pediatric Brain Tumor Patients Treated with Proton Therapy

Purpose

Pediatric brain tumor patients often experience significant cognitive sequelae. Resting-state functional MRI (rsfMRI) provides a measure of brain network organization, and we hypothesize that pediatric brain tumor patients treated with proton therapy will demonstrate abnormal brain network architecture related to cognitive outcome and radiation dosimetry.

Participants and Methods

Pediatric brain tumor patients treated with proton therapy were enrolled on a prospective study of cognitive assessment using the NIH Toolbox Cognitive Domain. rsfMRI was obtained in participants able to complete unsedated MRI. Brain system segregation (BSS), a measure of brain network architecture, was calculated for the whole brain, the high-level cognition association systems, and the sensory-motor systems.

Results

Twenty-six participants were enrolled in the study for cognitive assessment, and 18 completed rsfMRI. There were baseline cognitive deficits in attention and inhibition and processing speed prior to radiation with worsening performance over time in multiple domains. Average BSS across the whole brain was significantly decreased in participants compared with healthy controls (1.089 and 1.101, respectively; P = 0.001). Average segregation of association systems was significantly lower in participants than in controls (P < 0.001) while there was no difference in the sensory motor networks (P = 0.70). Right hippocampus dose was associated with worse attention and inhibition (P < 0.05) and decreased segregation in the dorsal attention network (P < 0.05).

Conclusion

Higher mean dose to the right hippocampus correlated with worse dorsal attention network segregation and worse attention and inhibition cognitive performance. Patients demonstrated alterations in brain network organization of association systems measured with rsfMRI; however, somatosensory system segregation was no different from healthy children. Further work with preradiation rsfMRI is needed to assess the effects of surgery and presence of a tumor on brain network architecture.

Reducing default mode network connectivity with mindfulness-based fMRI neurofeedback: a pilot study among adolescents with affective disorder history

Adolescents experience alarmingly high rates of major depressive disorder (MDD), however, gold-standard treatments are only effective for ~50% of youth. Accordingly, there is a critical need to develop novel interventions, particularly ones that target neural mechanisms believed to potentiate depressive symptoms. Directly addressing this gap, we developed mindfulness-based fMRI neurofeedback (mbNF) for adolescents that aims to reduce default mode network (DMN) hyperconnectivity, which has been implicated in the onset and maintenance of MDD. In this proof-of-concept study, adolescents (n = 9) with a lifetime history of depression and/or anxiety were administered clinical interviews and self-report questionnaires, and each participant's DMN and central executive network (CEN) were personalized using a resting state fMRI localizer. After the localizer scan, adolescents completed a brief mindfulness training followed by a mbNF session in the scanner wherein they were instructed to volitionally reduce DMN relative to CEN activation by practicing mindfulness meditation. Several promising findings emerged. First, mbNF successfully engaged the target brain state during neurofeedback; participants spent more time in the target state with DMN activation lower than CEN activation. Second, in each of the nine adolescents, mbNF led to significantly reduced within-DMN connectivity, which correlated with post-mbNF increases in state mindfulness. Last, a reduction of within-DMN connectivity mediated the association between better mbNF performance and increased state mindfulness. These findings demonstrate that personalized mbNF can effectively and non-invasively modulate the intrinsic networks associated with the emergence and persistence of depressive symptoms during adolescence.

Associations with youth psychotic-like experiences over time: Evidence for trans-symptom and specific cognitive and neural risk factors

The current study examined whether impairments in cognitive and neural factors at baseline (ages 9-10) predict initial levels or changes in psychotic-like experiences (PLEs) and whether such impairments generalize to other psychopathology symptoms (i.e., internalizing and externalizing symptoms). Using unique longitudinal Adolescent Brain Cognitive Development Study data, the study examined three time points from ages 9 to 13. Univariate latent growth models examined associations between baseline cognitive and neural metrics with symptom measures using discovery (n = 5,926) and replication (n = 5,952) data sets. For symptom measures (i.e., PLEs, internalizing, externalizing), we examined mean initial levels (i.e., intercepts) and changes over time (i.e., slopes). Predictors included neuropsychological test performance, global structural MRI, and several a priori within-network resting-state functional connectivity metrics. Results showed a pattern whereby baseline cognitive and brain metric impairments showed the strongest associations with PLEs over time. Lower cognitive, volume, surface area, and cingulo-opercular within-network connectivity metrics showed associations with increased PLEs and higher initial levels of externalizing and internalizing symptoms. Several metrics were uniquely associated with PLEs, including lower cortical thickness with higher initial PLEs and lower default mode network connectivity with increased PLEs slopes. Neural and cognitive impairments in middle childhood were broadly associated with increased PLEs over time, and showed stronger associations with PLEs compared with other psychopathology symptoms. The current study also identified markers potentially uniquely associated with PLEs (e.g., cortical thickness). Impairments in broad cognitive metrics, brain volume and surface area, and a network associated with information integration may represent risk factors for general psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Functional connectivity analysis of childhood depressive symptoms

BACKGROUND

Childhood depression is a highly distinct and prevalent condition with an unknown neurobiological basis. We wish to explore the resting state fMRI data in children for potential associations between neural connectivity and childhood depressive symptoms.

METHODS

A longitudinal birth cohort study with neuroimaging data obtained at 4.5, 6.0 and 7.5 years of age and the Children Depression Inventory 2 (CDI) administered between 8.5 and 10.5 years was used. The CDI score was used as the dependent variable and tested for correlation, both simple Pearson and network based statistic, with the functional connectivity values obtained from the resting state fMRI. Cross-validated permutation testing with a general linear model was used to validate that the identified functional connections were indeed implicated in childhood depression.

RESULTS

Ten functional connections and four brain regions (Somatomotor Area B, Temporoparietal Junction, Orbitofrontal Cortex and Insula) were identified as significantly associated with childhood depressive symptoms for girls at 6.0 and 7.5 years. No significant functional connections were found in girls at 4.5 years or for boys at any timepoint. Network based statistic and permutation testing confirmed these findings.

CONCLUSIONS

This study revealed significant sex-dependent associations of neural connectivity and childhood depressive symptoms. The regions identified are implicated in speech/language, social cognition and information integration and suggest unique pathways to childhood depressive symptoms.

Complementary and Integrative Treatments for Attention-Deficit/Hyperactivity Disorder in Youth

First-line psychopharmacologic and psychosocial treatments for attention-deficit/hyperactivity disorder in children are effective but limited by tolerability and accessibility problems. Many complementary and integrative strategies have been investigated as alternative or adjunctive treatments for the disorder, and the literature has progressed to meta-analyses for several. Although heterogeneity of study methods and risk of bias pervades the literature, we conclude that Omega-3 supplementation, dietary restriction of artificial food colorings, and physical activity can be considered evidence-based. Additionally, meditation, yoga, and sleep hygiene are safe, partially effective, cost effective and sensible adjunctive treatment strategies.

Identifying individuals with attention‐deficit/hyperactivity disorder based on multisite resting‐state functional magnetic resonance imaging: A radiomics analysis

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age-inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting-state functional magnetic resonance (rs-fMRI) have more discriminative power for the diagnosis of ADHD. The rs-fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD-200 Consortium. A total of four preprocessed rs-fMRI images including regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), voxel-mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve = 0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs-fMRI information to distinguish ADHD from healthy controls. The rs-fMRI-based radiomics features have the potential to be neuroimaging biomarkers for ADHD.

Developmental dyslexia, developmental coordination disorder and comorbidity discrimination using multimodal structural and functional neuroimaging

Developmental dyslexia (DD) and developmental coordination disorder (DCD) are two common neurodevelopmental disorders with a high co-occurrence rate. This led several authors to postulate that the two disorders share, at least partially, similar neural underpinning. However, even though several studies examined brain differences between typically developing (TD) children and children with either DD or DCD, no previous study directly compared DD, DCD and children with both disorders (COM) using neuroimaging. We acquired structural and resting-state functional MRI images of 136 children (TD = 42, DD = 45, DCD = 20, COM = 29). Difference between TD children and the other groups was assessed using univariate analysis of structural indexes including grey and white matter volumes and functional indexes quantifying activity (fraction of the amplitude of the low frequency fluctuations), local and global connectivity. Regional differences in structural and functional brain indexes were then used to train machine learning models to discriminate among DD, DCD and COM and to find the most discriminant regions. While no imaging index alone discriminated between the three groups, grouping grey and white matter volumes (structural model) or activity, local and global connectivity (functional model) made possible to discriminate among the DD, DCD and COM groups. The most important discrimination was obtained using the functional model, with regions in the cerebellum and the temporal lobe being the most discriminant for DCD and DD children, respectively. Results further showed that children with both DD and DCD have subtle but identifiable brain differences that can only be captured using several imaging indexes pertaining to both brain structure and function.

The update of self-identity: Importance of assessing autobiographical memory in major depressive disorder

Major depressive disorder is a leading global cause of disability. There is a growing interest for memory in mood disorders since it might constitute an original tool for prevention, diagnosis, and treatment. MDD is associated with impaired autobiographical memory characterized by a tendency to overgeneral memory, rather than vivid episodic self-defining memory, which is mandatory for problem-solving and projection in the future. This memory bias is maintained by three mechanisms: ruminations, avoidance, and impaired executive control. If we adopt a broader and comprehensive perspective, we can hypothesize that all those alterations have the potential to impair self-identity updating. We posit that this update requires a double referencing process: (1) to internalized self-representation and (2) to an externalized framework dealing with the representation of the consequence of actions. Diagnostic and therapeutic implications are discussed in the light of this model and the importance of assessing autobiographical memory in MDD is highlighted. This article is categorized under: Psychology > Memory Psychology > Brain Function and Dysfunction Neuroscience > Clinical.

Decoding the radiomic features of dorsolateral prefrontal cortex in individuals with accelerated cortical changes: implications for personalized transcranial magnetic stimulation

Purpose

Image-guided transcranial magnetic stimulation (TMS) is an emerging research field in neuroscience and rehabilitation medicine. Cortical morphometry, as a radiomic phenotype of aging, plays a vital role in developing personalized TMS model, yet few studies are afoot to examine the aging effects on region-specific morphometry and use it in the estimation of TMS-induced electric fields. Our study was aimed to investigate the radiomic features of bilateral dorsolateral prefrontal cortex (DLPFC) and quantify the TMS-induced electric fields during aging.

Approach

Baseline, 1-year and 3-year structural magnetic resonance imaging (MRI) scans from normal aging (NA) adults ( n = 32 ) and mild cognitive impairment (MCI) converters ( n = 22 ) were drawn from the Open Access Series of Imaging Studies. The quantitative measures of radiomics included cortical thickness, folding, and scalp-to-cortex distance. Realistic head models were developed to simulate the impacts of radiomic features on TMS-induced E-fields using the finite-element method.

Results

A pronounced aging-related decrease was found in the gyrification of left DLPFC in MCI converters ( t = 2.21 , p = 0.035 ), which could predict the decline of global cognition at 3-year follow up. Along with the decreased gyrification in left DLPFC, the magnitude of TMS-induced E-fields was rapidly decreased in MCI converters ( t = 2.56 , p = 0.018 ).

Conclusions

MRI-informed radiomic features of the treatment targets have significant effects on the intensity and distribution of the stimulation-induced electric fields in prodromal dementia patients. Our findings highlight the importance of region-specific radiomics when conducting the transcranial brain stimulation in individuals with accelerated cortical changes, such as Alzheimer's disease.

Global hippocampus functional connectivity as a predictive neural marker for conversion to future mood disorder in unaffected offspring of bipolar disorder parents

OBJECTIVES

Hippocampus-related functional alteration in genetically at-risk individuals may reflect an endophenotype of a mood disorder. Herein, we performed a prospective study to investigate whether baseline hippocampus functional connectivity (FC) in offspring of patients with bipolar disorder (BD) would predict subsequent conversion to mood disorder.

METHODS

Eighty bipolar offspring and 40 matched normal controls (NC) underwent resting state functional MRI (rsfMRI) scanning on a 3.0 Tesla MR scanner. The offspring were subdivided into asymptomatic offspring (AO) (n = 41) and symptomatic offspring (SO) (n = 39) according to whether they manifested subthreshold mood symptoms. After identifying the different hippocampus FCs between the AO and SO, a logistic regression analysis was conducted to investigate whether the baseline hippocampus FCs predicted a future mood disorder during a 6-year follow-up.

RESULTS

We identified seven baseline para/hippocampus FCs that showed differences between AO and SO, which were entered as predictive features in the logistic regressive model. Of the 80 bipolar offspring entering the analysis, the FCs between left hippocampus and left precuneus, and between right hippocampus and left posterior cingulate, showed a discriminative capacity for predicting future mood disorder (area-under-curve, or AUC=75.76 % and 75.00 % respectively), and for predicting BD onset (AUC=77.46 % and 81.63 %, respectively).

CONCLUSIONS

The present findings revealed high predictive utility of the hippocampus resting state FCs for future mood disorder and BD onset in individuals at familial risk. These neural markers can potentially improve early detection of individuals carrying particularly high risk for future mood disorder.

Can machine learning identify childhood characteristics that predict future development of bipolar disorder a decade later?

Early identification of bipolar disorder may provide appropriate support and treatment, however there is no current evidence for statistically predicting whether a child will develop bipolar disorder. Machine learning methods offer an opportunity for developing empirically-based predictors of bipolar disorder. This study examined whether bipolar disorder can be predicted using clinical data and machine learning algorithms. 492 children, ages 6-18 at baseline, were recruited from longitudinal case-control family studies. Participants were assessed at baseline, then followed-up after 10 years. In addition to sociodemographic data, children were assessed with psychometric scales, structured diagnostic interviews, and cognitive and social functioning assessments. Using the Balanced Random Forest algorithm, we examined whether the diagnostic outcome of full or subsyndromal bipolar disorder could be predicted from baseline data. 45 children (10%) developed bipolar disorder at follow-up. The model predicted subsequent bipolar disorder with 75% sensitivity, 76% specificity, and an Area Under the Receiver Operating Characteristics of 75%. Predictors best differentiating between children who did or did not develop bipolar disorder were the Child Behavioral Checklist Externalizing and Internalizing behaviors, the Child Behavioral Checklist Total t-score, problematic school functions indexed through the Child Behavioral Checklist School Competence scale, and the Child Behavioral Checklist Anxiety/Depression and Aggression scales. Our study provides the first quantitative model to predict bipolar disorder. Longitudinal prediction may help clinicians assess children with emergent psychopathology for future risk of bipolar disorder, an area of clinical and scientific importance. Machine learning algorithms could be implemented to alert clinicians to risk for bipolar disorder.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Altered Resting-State Network in Adolescents with Problematic Internet Use

Problematic internet use (PIU) is increasingly recognized as a mental health concern, particularly among adolescents. The resting-state functional connectivity (rsFC) of the triple-network model has been described inconsistently in PIU. Using resting-state fMRI (rsFMRI) and hypothesizing a lower rsFC between default mode (DMN) and central executive networks (CEN) but a higher rsFC within the salience network (SN), this study scrutinized the neural substrates of PIU adolescents. A total of 30 adolescents with PIU and 30 control subjects underwent rsFMRI. The severity of PIU was evaluated by the Internet Addiction Test. Additionally, personality traits as well as emotional and behavioral problems were evaluated by the Temperament and Character Inventory (TCI) and the Strength and Difficulties Questionnaire (SDQ), respectively. Focusing on the DMN, SN, and CEN, we compared rsFC values between PIU and the control. Subsequently, within the combined group of subjects, TCI and SDQ correlation and mediation effects were investigated. Higher rsFC values of the left lateral prefrontal cortex (LPFC(L)) with the left anterior insula (aIns(L)) were observed for PIU than for the control, while rsFCs of the LPFC(L) with the medial PFC (MPFC), LPFC(L), as well as with the right lateral parietal cortex (LP(R)) were lower for PIU. Among these significant group differences, the rsFC between the LPFC(L) and MPFC was mediated by emotional symptoms (standardized β = −0.12, 95% CI −0.29, −0.0052). The dysfunctional attention switching and incentive salience regulated by the SN were implicated as being a neural correlate of PIU, and this relationship would in part be explained by the emotional dysregulation associated with PIU in adolescents.

Altered functional connectivity in first-episode and recurrent depression: A resting-state functional magnetic resonance imaging study

Background

Functional magnetic resonance imaging (fMRI) studies examining differences in the activity of brain networks between the first depressive episode (FDE) and recurrent depressive episode (RDE) are limited. The current study observed and compared the altered functional connectivity (FC) characteristics in the default mode network (DMN), cognitive control network (CCN), and affective network (AN) between the RDE and FDE. In addition, we further investigated the correlation between abnormal FC and clinical symptoms.

Methods

We recruited 32 patients with the RDE, 31 patients with the FDE, and 30 healthy controls (HCs). All subjects underwent resting-state fMRI. The seed-based FC method was used to analyze the abnormal brain networks in the DMN, CCN, and AN among the three groups and further explore the correlation between abnormal FC and clinical symptoms.

Results

One-way analysis of variance showed significant differences the FC in the DMN, CCN, and AN among the three groups in the frontal, parietal, temporal, and precuneus lobes and cerebellum. Compared with the RDE group, the FDE group generally showed reduced FC in the DMN, CCN, and AN. Compared with the HC group, the FDE group showed reduced FC in the DMN, CCN, and AN, while the RDE group showed reduced FC only in the DMN and AN. Moreover, the FC in the left posterior cingulate cortices and the right inferior temporal gyrus in the RDE group were positively correlated with the 17-item Hamilton Rating Scale for Depression (HAMD-17), and the FC in the left dorsolateral prefrontal cortices and the right precuneus in the FDE group were negatively correlated with the HAMD-17.

Conclusions

The RDE and FDE groups showed multiple abnormal brain networks. However, the alterations of abnormal FC were more extensive and intensive in the FDE group.

The Role of Visual Experience in Individual Differences of Brain Connectivity

Visual cortex organization is highly consistent across individuals. But to what degree does this consistency depend on life experience, in particular sensory experience? In this study, we asked whether visual cortex reorganization in congenital blindness results in connectivity patterns that are particularly variable across individuals, focusing on resting-state functional connectivity (RSFC) patterns from the primary visual cortex. We show that the absence of shared visual experience results in more variable RSFC patterns across blind individuals than sighted controls. Increased variability is specifically found in areas that show a group difference between the blind and sighted in their RSFC. These findings reveal a relationship between brain plasticity and individual variability; reorganization manifests variably across individuals. We further investigated the different patterns of reorganization in the blind, showing that the connectivity to frontal regions, proposed to have a role in the reorganization of the visual cortex of the blind toward higher cognitive roles, is highly variable. Further, we link some of the variability in visual-to-frontal connectivity to another environmental factor-duration of formal education. Together, these findings show a role of postnatal sensory and socioeconomic experience in imposing consistency on brain organization. By revealing the idiosyncratic nature of neural reorganization, these findings highlight the importance of considering individual differences in fitting sensory aids and restoration approaches for vision loss.SIGNIFICANCE STATEMENT The typical visual system is highly consistent across individuals. What are the origins of this consistency? Comparing the consistency of visual cortex connectivity between people born blind and sighted people, we showed that blindness results in higher variability, suggesting a key impact of postnatal individual experience on brain organization. Further, connectivity patterns that changed following blindness were particularly variable, resulting in diverse patterns of brain reorganization. Individual differences in reorganization were also directly affected by nonvisual experiences in the blind (years of formal education). Together, these findings show a role of sensory and socioeconomic experiences in creating individual differences in brain organization and endorse the use of individual profiles for rehabilitation and restoration of vision loss.

Neurobiological, familial and genetic risk factors for dimensional psychopathology in the Adolescent Brain Cognitive Development study

BACKGROUND

Adolescence is a key period for brain development and the emergence of psychopathology. The Adolescent Brain Cognitive Development (ABCD) study was created to study the biopsychosocial factors underlying healthy and pathological brain development during this period, and comprises the world's largest youth cohort with neuroimaging, family history and genetic data.

METHODS

We examined 9856 unrelated 9-to-10-year-old participants in the ABCD study drawn from 21 sites across the United States, of which 7662 had multimodal magnetic resonance imaging scans passing quality control, and 4447 were non-Hispanic white and used for polygenic risk score analyses. Using data available at baseline, we associated eight 'syndrome scale scores' from the Child Behavior Checklist-summarizing anxious/depressed symptoms, withdrawn/depressed symptoms, somatic complaints, social problems, thought problems, attention problems, rule-breaking behavior, and aggressive behavior-with resting-state functional and structural brain magnetic resonance imaging measures; eight indicators of family history of psychopathology; and polygenic risk scores for major depression, bipolar disorder, schizophrenia, attention deficit hyperactivity disorder (ADHD) and anorexia nervosa. As a sensitivity analysis, we excluded participants with clinically significant (>97th percentile) or borderline (93rd-97th percentile) scores for each dimension.

RESULTS

Most Child Behavior Checklist dimensions were associated with reduced functional connectivity within one or more of four large-scale brain networks-default mode, cingulo-parietal, dorsal attention, and retrosplenial-temporal. Several dimensions were also associated with increased functional connectivity between the default mode, dorsal attention, ventral attention and cingulo-opercular networks. Conversely, almost no global or regional brain structural measures were associated with any of the dimensions. Every family history indicator was associated with every dimension. Major depression polygenic risk was associated with six of the eight dimensions, whereas ADHD polygenic risk was exclusively associated with attention problems and externalizing behavior (rule-breaking and aggressive behavior). Bipolar disorder, schizophrenia and anorexia nervosa polygenic risk were not associated with any of the dimensions. Many associations remained statistically significant even after excluding participants with clinically significant or borderline psychopathology, suggesting that the same risk factors that contribute to clinically significant psychopathology also contribute to continuous variation within the clinically normal range.

CONCLUSIONS

This study codifies neurobiological, familial and genetic risk factors for dimensional psychopathology across a population-scale cohort of community-dwelling preadolescents. Future efforts are needed to understand how these multiple modalities of risk intersect to influence trajectories of psychopathology into late adolescence and adulthood.

Exploring neural correlates of behavioral and academic resilience among children in poverty

Children in poverty must contend with systems that do not meet their needs. We explored what, at a neural level, helps explain children's resilience in these contexts. Lower coupling between lateral frontoparietal network (LFPN) and default mode network (DMN)-linked, respectively, to externally- and internally-directed thought-has previously been associated with better cognitive performance. However, we recently found the opposite pattern for children in poverty. Here, we probed ecologically-valid assessments of performance. In a pre-registered study, we investigated trajectories of network coupling over ages 9-13 and their relation to school grades and attention problems. We analyzed longitudinal data from ABCD Study (N = 8366 children at baseline; 1303 below poverty). The link between cognitive performance and grades was weaker for children in poverty, highlighting the importance of ecologically-valid measures. As predicted, higher LFPN-DMN connectivity was linked to worse grades and attentional problems for children living above poverty, while children below poverty showed opposite tendencies. This interaction between LFPN-DMN connectivity and poverty related to children's grades two years later; however, it was attenuated when controlling for baseline grades and was not related to attention longitudinally. Together, these findings suggest network connectivity is differentially related to performance in real-world settings for children above and below poverty.

Threat imminence reveals links among unfolding of anticipatory physiological response, cortical-subcortical intrinsic functional connectivity, and anxiety

Excessive expression of fear responses in anticipation of threat occurs in anxiety, but understanding of underlying pathophysiological mechanisms is limited. Animal research indicates that threat-anticipatory defensive responses are dynamically organized by threat imminence and rely on conserved circuitry. Insight from basic neuroscience research in animals on threat imminence could guide mechanistic research in humans mapping abnormal function in this circuitry to aberrant defensive responses in pathological anxiety. 50 pediatric anxiety patients and healthy-comparisons (33 females) completed an instructed threat-anticipation task whereby cues signaled delivery of painful (threat) or non-painful (safety) thermal stimulation. Temporal changes in skin-conductance indexed anxiety effects on anticipatory responding as function of threat imminence. Multivariate network analyses of resting-state functional connectivity data from a subsample were used to identify intrinsic-function correlates of anticipatory-response dynamics, within a specific, distributed network derived from translational research on defensive responding. By considering threat imminence, analyses revealed specific anxiety effects. Importantly, pathological anxiety was associated with excessive deployment of anticipatory physiological response as threat, but not safety, outcomes became more imminent. Magnitude of increase in threat-anticipatory physiological responses corresponded with magnitude of intrinsic connectivity within a cortical-subcortical circuit. Moreover, more severe anxiety was associated with stronger associations between anticipatory physiological responding and connectivity that ventromedial prefrontal cortex showed with hippocampus and basolateral amygdala, regions implicated in animal models of anxiety. These findings link basic and clinical research, highlighting variations in intrinsic function in conserved defensive circuitry as a potential pathophysiological mechanism in anxiety.

Brain network coupling associated with cognitive performance varies as a function of a child's environment in the ABCD study

Prior research indicates that lower resting-state functional coupling between two brain networks, lateral frontoparietal network (LFPN) and default mode network (DMN), relates to cognitive test performance, for children and adults. However, most of the research that led to this conclusion has been conducted with non-representative samples of individuals from higher-income backgrounds, and so further studies including participants from a broader range of socioeconomic backgrounds are required. Here, in a pre-registered study, we analyzed resting-state fMRI from 6839 children ages 9-10 years from the ABCD dataset. For children from households defined as being above poverty (family of 4 with income > $25,000, or family of 5+ with income > $35,000), we replicated prior findings; that is, we found that better performance on cognitive tests correlated with weaker LFPN-DMN coupling. For children from households defined as being in poverty, the direction of association was reversed, on average: better performance was instead directionally related to stronger LFPN-DMN connectivity, though there was considerable variability. Among children in households below poverty, the direction of this association was predicted in part by features of their environments, such as school type and parent-reported neighborhood safety. These results highlight the importance of including representative samples in studies of child cognitive development.

What have we really learned from functional connectivity in clinical populations?

Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.

Predicting the course of ADHD symptoms through the integration of childhood genomic, neural, and cognitive features

Childhood attention deficit hyperactivity disorder (ADHD) shows a highly variable course with age: some individuals show improving, others stable or worsening symptoms. The ability to predict symptom course could help individualize treatment and guide interventions. By studying a cohort of 362 youth, we ask if polygenic risk for ADHD, combined with baseline neural and cognitive features could aid in the prediction of the course of symptoms over an average period of 4.8 years. Compared to a never-affected comparison group, we find that participants with worsening symptoms carried the highest polygenic risk for ADHD, followed by those with stable symptoms, then those whose symptoms improved. Participants with worsening symptoms also showed atypical baseline cognition. Atypical microstructure of the cingulum bundle and anterior thalamic radiation was associated with improving symptoms while reduction of thalamic volume was found in those with stable symptoms. Machine-learning algorithms, trained and tested on independent groups, performed well in classifying those never affected against groups with worsening, stable, and improving symptoms (area under the curve >0.79). We conclude that some measures of polygenic risk, cognition, and neuroimaging show significant associations with the future course of ADHD symptoms and may have modest predictive power. These features warrant further exploration as prognostic tools.

Multimodal Investigations of Reward Circuitry and Anhedonia in Adolescent Depression

Depression is a highly prevalent condition with devastating personal and public health consequences that often first manifests during adolescence. Though extensively studied, the pathogenesis of depression remains poorly understood, and efforts to stratify risks and identify optimal interventions have proceeded slowly. A major impediment has been the reliance on an all-or-nothing categorical diagnostic scheme based solely on whether a patient endorses an arbitrary number of common symptoms for a sufficiently long period. This approach masks the well-documented heterogeneity of depression, a disorder that is highly variable in presentation, severity, and course between individuals and is frequently comorbid with other psychiatric conditions. In this targeted review, we outline the limitations of traditional diagnosis-based research and instead advocate an alternative approach centered around symptoms as unique dimensions of clinical dysfunction that span across disorders and more closely reflect underlying neurobiological abnormalities. In particular, we highlight anhedonia-the reduced ability to anticipate and experience pleasure-as a specific, quantifiable index of reward dysfunction and an ideal candidate for dimensional investigation. Anhedonia is a core symptom of depression but also a salient feature of numerous other conditions, and its severity varies widely within clinical and even healthy populations. Similarly, reward dysfunction is a hallmark of depression but is evident across many psychiatric conditions. Reward function is especially relevant in adolescence, a period characterized by exaggerated reward-seeking behaviors and rapid maturation of neural reward circuitry. We detail extensive work by our research group and others to investigate the neural and systemic factors contributing to reward dysfunction in youth, including our cumulative findings using multiple neuroimaging and immunological measures to study depressed adolescents but also trans-diagnostic cohorts with diverse psychiatric symptoms. We describe convergent evidence that reward dysfunction: (a) predicts worse clinical outcomes, (b) is associated with functional and chemical abnormalities within and beyond the neural reward circuitry, (c) is linked to elevated peripheral levels of inflammatory biomarkers, and (d) manifests early in the course of illness. Emphasis is placed on high-resolution neuroimaging techniques, comprehensive immunological assays, and data-driven analyses to fully capture and characterize the complex, interconnected nature of these systems and their contributions to adolescent reward dysfunction.

The Habenula in the Link Between ADHD and Mood Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset, neurodevelopmental disorder, whereas major depressive disorder (MDD) is a mood disorder that typically emerges in adulthood. Accumulating evidence suggests that these seemingly unrelated psychiatric disorders, whose symptoms even appear antithetical [e.g., psychomotor retardation in depression vs. hyperactivity (psychomotor acceleration) in ADHD], are in fact associated with each other. Thus, individuals with ADHD exhibit high comorbidity with MDD later in life. Moreover, genetic studies have shown substantial overlaps of susceptibility genes between ADHD and MDD. Here, we propose a novel and testable hypothesis that the habenula, the epithalamic brain region important for the regulation of monoamine transmission, may be involved in both ADHD and MDD. The hypothesis suggests that an initially hypoactive habenula during childhood in individuals with ADHD may undergo compensatory changes during development, priming the habenula to be hyperactive in response to stress exposure and thereby increasing vulnerability to MDD in adulthood. Moreover, we propose a new perspective on habenular deficits in psychiatric disorders that consider the habenula a neural substrate that could explain multiple psychiatric disorders.

Adolescent Attention-Deficit/Hyperactivity Disorder: Understanding Teenage Symptom Trajectories

Symptoms of attention-deficit/hyperactivity disorder (ADHD) run a variable course through adolescence. While most affected individuals show some improvement, particularly of hyperactivity-impulsivity, symptoms of inattention are more persistent, and some individuals may meet diagnostic criteria for the first time during adolescence. Genetic factors affect adolescent symptom trajectories; those showing persistence likely carry a greater burden of common risk alleles. Rare structural genomic variants, such as copy number variants and point mutations, might also play a role. Although psychostimulant medication is associated with better functional outcomes, an impact on underlying adolescent symptom trajectories has been hard to demonstrate. At a neural level, several studies report that adolescents whose childhood ADHD symptoms have remitted are indistinguishable from neurotypical individuals. This finding could reflect the "carrying forward" of relatively typical childhood neural features among those destined for adolescent remission or the correction of early childhood anomalies with a convergence toward typical dimensions. Other studies have noted unique, possibly compensatory patterns of neural activity among adolescents whose ADHD has improved. Finally, different neural processes might occur in different brain regions. Thus, some functional imaging studies find that subcortical anomalies reflect the onset of ADHD and remain throughout life regardless of symptom change, whereas the variable clinical course of adolescent ADHD is determined by plasticity of the cerebral cortex. Integrating an understanding of the neural processes with genomic risk could elucidate the mechanisms underlying the complex course of adolescent ADHD.

Functional Connectivity Network Disruption Underlies Domain-Specific Impairments in Attention for Children Born Very Preterm

Attention problems are common in school-age children born very preterm (VPT; < 32 weeks gestational age), but the contribution of aberrant functional brain connectivity to these problems is not known. As part of a prospective longitudinal study, brain functional connectivity (fc) was assessed alongside behavioral measures of selective, sustained, and executive attention in 58 VPT and 65 full-term (FT) born children at corrected-age 12 years. VPT children had poorer sustained, shifting, and divided attention than FT children. Within the VPT group, poorer attention scores were associated with between-network connectivity in ventral attention, visual, and subcortical networks, whereas between-network connectivity in the frontoparietal, cingulo-opercular, dorsal attention, salience and motor networks was associated with attention functioning in FT children. Network-level differences were also evident between VPT and FT children in specific attention domains. Findings contribute to our understanding of fc networks that potentially underlie typical attention development and suggest an alternative network architecture may help support attention in VPT children.

Associations Between Resting-State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood

BACKGROUND

Previous research from the Adolescent Brain Cognitive Development (ABCD) Study delineated and validated a hierarchical 5-factor structure with a general psychopathology (p) factor at the apex and 5 specific factors (internalizing, somatoform, detachment, neurodevelopmental, externalizing) using parent-reported child symptoms. The present study is the first to examine associations between dimensions from a hierarchical structure and resting-state functional connectivity (RSFC) networks.

METHODS

Using 9- to 11-year-old children from the ABCD Study baseline sample, we examined the variance explained by each hierarchical structure level (p-factor, 2-factor, 3-factor, 4-factor, and 5-factor models) in associations with RSFC. Analyses were first conducted in a discovery dataset (n = 3790), and significant associations were examined in a replication dataset (n = 3791).

RESULTS

There were robust associations between the p-factor and lower connectivity within the default mode network, although stronger effects emerged for the neurodevelopmental factor. Neurodevelopmental impairments were also related to variation in RSFC networks associated with attention to internal states and external stimuli. Analyses revealed robust associations between the neurodevelopmental dimension and several RSFC metrics, including within the default mode network, between the default mode network with cingulo-opercular and "Other" (unassigned) networks, and between the dorsal attention network with the Other network.

CONCLUSIONS

The hierarchical structure of psychopathology showed replicable links to RSFC associations in middle childhood. The specific neurodevelopmental dimension showed robust associations with multiple RSFC metrics. These results show the utility of examining associations between intrinsic brain architecture and specific dimensions of psychopathology, revealing associations especially with neurodevelopmental impairments.

Intrinsic Cortico-Subcortical Functional Connectivity in Developmental Dyslexia and Developmental Coordination Disorder

Developmental dyslexia (DD) and developmental coordination disorder (DCD) are distinct diagnostic disorders. However, they also frequently co-occur and may share a common etiology. It was proposed conceptually a neural network framework that explains differences and commonalities between DD and DCD through impairments of distinct or intertwined cortico-subcortical connectivity pathways. The present study addressed this issue by exploring intrinsic cortico-striatal and cortico-cerebellar functional connectivity in a large (n = 136) resting-state fMRI cohort study of 8–12-year-old children with typical development and with DD and/or DCD. We delineated a set of cortico-subcortical functional circuits believed to be associated with the brain’s main functions (visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal control, and default-mode). Next, we assessed, using general linear and multiple kernel models, whether and which circuits distinguished between the groups. Findings revealed that somatomotor cortico-cerebellar and frontoparietal cortico-striatal circuits are affected in the presence of DCD, including abnormalities in cortico-cerebellar connections targeting motor-related regions and cortico-striatal connections mapping onto posterior parietal cortex. Thus, DCD but not DD may be considered as an impairment of cortico-subcortical functional circuits.

Top ten discoveries of the year: Neurodevelopmental disorders

Developmental brain disorders, a highly heterogeneous group of disorders with a prevalence of around 3% of worldwide population, represent a growing medical challenge. They are characterized by impaired neurodevelopmental processes leading to deficits in cognition, social interaction, behavior and motor functioning as a result of abnormal development of brain. This can include developmental brain dysfunction, which can manifest as neuropsychiatric problems or impaired motor function, learning, language or non-verbal communication. Several of these phenotypes can often co-exist in the same patient and characterize the same disorder. Here I discuss some contributions in 2019 that are shaking our basic understanding of the pathogenesis of neurodevelopmental disorders. Recent developments in sophisticated in-utero imaging diagnostic tools have raised the possibility of imaging the fetal human brain growth, providing insights into the developing anatomy and improving diagnostics but also allowing a better understanding of antenatal pathology. On the other hand, advances in our understanding of the pathogenetic mechanisms reveal a remarkably complex molecular neuropathology involving a myriad of genetic architectures and regulatory elements that will help establish more rigorous genotype-phenotype correlations.