INTRODUCTION: The human cerebellum emerges as a posterior brain structure integrating neural networks for sensorimotor, cognitive, and emotional processing across the lifespan. Developmental studies of the cerebellar anatomy and function are scant. We examine age-dependent MRI morphometry of the anterior cerebellar vermis, lobules I-V and posterior neocortical lobules VI-VII and their relationship to sensorimotor and cognitive functions. METHODS: Typically developing children (TDC; n=38; age 9-15) and healthy adults (HAC; n=31; 18-40) participated in high-resolution MRI. Rigorous anatomically informed morphometry of the vermis lobules I-V and VI-VII and total brain volume (TBV) employed manual segmentation computer-assisted FreeSurfer Image Analysis Program [http://surfer.nmr.mgh.harvard.edu]. The neuropsychological scores (WASI-II) were normalized and related to volumes of anterior, posterior vermis, and TBV. RESULTS: TBVs were age independent. Volumes of I-V and VI-VII were significantly reduced in TDC. The ratio of VI-VII to I-V (â¼60%) was stable across age-groups; I-V correlated with visual-spatial-motor skills; VI-VII with verbal, visual-abstract and FSIQ. CONCLUSIONS: In TDC neither anterior I-V nor posterior VI-VII vermis attained adult volumes. The "inverted U" developmental trajectory of gray matter peaking in adolescence does not explain this finding. The hypothesis of protracted development of oligodendrocyte/myelination is suggested as a contributor to TDC's lower cerebellar vermis volumes.
Throughout childhood and adolescence, the brain undergoes significant structural and functional changes that contribute to the maturation of multiple cognitive domains, including selective attention. Selective attention is crucial for healthy executive functioning and while key brain regions serving selective attention have been identified, their age-related changes in neural oscillatory dynamics and connectivity remain largely unknown. We examined the developmental sensitivity of selective attention circuitry in 91 typically developing youth aged 6 - 13 years old. Participants completed a number-based Simon task while undergoing magnetoencephalography (MEG) and the resulting data were preprocessed and transformed into the time-frequency domain. Significant oscillatory brain responses were imaged using a beamforming approach, and task-related peak voxels in the occipital, parietal, and cerebellar cortices were used as seeds for subsequent whole-brain connectivity analyses in the alpha and gamma range. Our key findings revealed developmentally sensitive connectivity profiles in multiple regions crucial for selective attention, including the temporoparietal junction (alpha) and prefrontal cortex (gamma). Overall, these findings suggest that brain regions serving selective attention are highly sensitive to developmental changes during the pubertal transition period.
At rest children with prenatal alcohol exposure (PAE) exhibit impaired static and dynamic functional connectivity, along with decreased alpha oscillations. Sex-specific information regarding the impact of PAE on whole-brain resting-state gamma spectral power remains unknown. Eyes-closed and eyes-open MEG resting-state data were examined in 83 children, ages 6-13 years of age. Using a matched design, the sample consisted of 42 typically developing children (TDC) (22 males/20 females) and 41 children with PAE and/or a fetal alcohol spectrum disorders (FASD) diagnosis (21 males/20 females). Whole-brain source resting-state spectral power was examined to determine group and sex specific relationships. Within gamma, we found sex and group specific changes such that female participants with PAE/FASD had increased gamma power when compared to female TDC and male participants with PAE/FASD. These differences were detected in most source regions analyzed during both resting-states, and were observed across the age spectrum examined. Within delta, we found sex and group specific changes such that female participants with PAE/FASD had decreased delta power when compared to female TDC and male participants with PAE/FASD. The reduced delta oscillations in female participants with PAE/FASD were detected in several source regions during eyes-closed rest and were evident at younger ages. These results indicate PAE alters neural oscillations during rest in a sex-specific manner, with females with PAE/FASD showing the largest perturbations. These results further demonstrate PAE has global effects on resting-state spectral power and connectivity, creating long-term consequences by potentially disrupting the excitation/inhibition balance in the brain, interrupting normative neurodevelopment.
Fetal Alcohol Spectrum Disorders , Prenatal Exposure Delayed Effects , Child , Humans , Male , Female , Pregnancy , Brain , Sex Characteristics
Numerous investigations have characterized the oscillatory dynamics serving working memory in adults, but few have probed its relationship with chronological age in developing youth. We recorded magnetoencephalography during a modified Sternberg verbal working memory task in 82 youth participants aged 6-14 years old. Significant oscillatory responses were identified and imaged using a beamforming approach and the resulting whole-brain maps were probed for developmental effects during the encoding and maintenance phases. Our results indicated robust oscillatory responses in the theta (4-7 Hz) and alpha (8-14 Hz) range, with older participants exhibiting stronger alpha oscillations in left-hemispheric language regions. Older participants also had greater occipital theta power during encoding. Interestingly, there were sex-by-age interaction effects in cerebellar cortices during encoding and in the right superior temporal region during maintenance. These results extend the existing literature on working memory development by showing strong associations between age and oscillatory dynamics across a distributed network. To our knowledge, these findings are the first to link chronological age to alpha and theta oscillatory responses serving working memory encoding and maintenance, both across and between male and female youth; they reveal robust developmental effects in crucial brain regions serving higher order functions.
OBJECTIVE: Brain function is understood to be regulated by complex spatiotemporal dynamics, and can be characterized by a combination of observed brain response patterns in time and space. Magnetoencephalography (MEG), with its high temporal resolution, and functional magnetic resonance imaging (fMRI), with its high spatial resolution, are complementary imaging techniques with great potential to reveal information about spatiotemporal brain dynamics. Hence, the complementary nature of these imaging techniques holds much promise to study brain function in time and space, especially when the two data types are allowed to fully interact. METHODS: We employed coupled tensor/matrix factorization (CMTF) to extract joint latent components in the form of unique spatiotemporal brain patterns that can be used to study brain development and function on a millisecond scale. RESULTS: Using the CMTF model, we extracted distinct brain patterns that revealed fine-grained spatiotemporal brain dynamics and typical sensory processing pathways informative of high-level cognitive functions in healthy adolescents. The components extracted from multimodal tensor fusion possessed better discriminative ability between high- and low-performance subjects than single-modality data-driven models. CONCLUSION: Multimodal tensor fusion successfully identified spatiotemporal brain dynamics of brain function and produced unique components with high discriminatory power. SIGNIFICANCE: The CMTF model is a promising tool for high-order, multimodal data fusion that exploits the functional resolution of MEG and fMRI, and provides a comprehensive picture of the developing brain in time and space.
Background: Psychosocial distress among youth is a major public health issue characterized by disruptions in cognitive control processing. Using the National Institute of Mental Health's Research Domain Criteria (RDoC) framework, we quantified multidimensional neural oscillatory markers of psychosocial distress serving cognitive control in youth. Methods: The sample consisted of 39 peri-adolescent participants who completed the NIH Toolbox Emotion Battery (NIHTB-EB) and the Eriksen flanker task during magnetoencephalography (MEG). A psychosocial distress index was computed with exploratory factor analysis using assessments from the NIHTB-EB. MEG data were analyzed in the time-frequency domain and peak voxels from oscillatory maps depicting the neural cognitive interference effect were extracted for voxel time series analyses to identify spontaneous and oscillatory aberrations in dynamics serving cognitive control as a function of psychosocial distress. Further, we quantified the relationship between psychosocial distress and dynamic functional connectivity between regions supporting cognitive control. Results: The continuous psychosocial distress index was strongly associated with validated measures of pediatric psychopathology. Theta-band neural cognitive interference was identified in the left dorsolateral prefrontal cortex (dlPFC) and middle cingulate cortex (MCC). Time series analyses of these regions indicated that greater psychosocial distress was associated with elevated spontaneous activity in both the dlPFC and MCC and blunted theta oscillations in the MCC. Finally, we found that stronger phase coherence between the dlPFC and MCC was associated with greater psychosocial distress. Conclusions: Greater psychosocial distress was marked by alterations in spontaneous and oscillatory theta activity serving cognitive control, along with hyperconnectivity between the dlPFC and MCC.
Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.
Brain , Cognition , Humans , Brain/diagnostic imaging , Diffusion , Walking , Magnetic Resonance Imaging
The transition from childhood to adolescence is associated with an influx of sex hormones, which not only facilitates physical and behavioral changes, but also dramatic changes in neural circuitry. While previous work has shown that pubertal hormones modulate structural and functional brain development, few of these studies have focused on the impact that such hormones have on spontaneous cortical activity, and whether these effects are modulated by sex during this critical developmental window. Herein, we examined the effect of endogenous testosterone on spontaneous cortical activity in 71 typically-developing youth (ages 10-17 years; 32 male). Participants completed a resting-state magnetoencephalographic (MEG) recording, structural MRI, and provided a saliva sample for hormone analysis. MEG data were source-reconstructed and the power within five canonical frequency bands (delta, theta, alpha, beta, and gamma) was computed. The resulting power spectral density maps were analyzed via vertex-wise ANCOVAs to identify spatially specific effects of testosterone and sex by testosterone interactions, while covarying out age. We found robust sex differences in the modulatory effects of testosterone on spontaneous delta, beta, and gamma activity. These interactions were largely confined to frontal cortices and exhibited a stark switch in the directionality of the correlation from the low (delta) to high frequencies (beta/gamma). For example, in the delta band, greater testosterone related to lower relative power in prefrontal cortices in boys, while the reverse pattern was found for girls. These data suggest testosterone levels are uniquely related to the development of spontaneous cortical dynamics during adolescence, and such levels are associated with different developmental patterns in males and females within regions implicated in executive functioning.
Magnetoencephalography , Testosterone , Adolescent , Humans , Male , Female , Child , Testosterone/pharmacology , Magnetic Resonance Imaging , Frontal Lobe , Prefrontal Cortex/diagnostic imaging , Brain
INTRODUCTION: The anterior pituitary gland (PG) is a potential locus of hypothalamic-pituitary-adrenal (HPA) axis responsivity to early life stress, with documented associations between dehydroepiandrosterone (DHEA) levels and anterior PG volumes. In adults, elevated anxiety/depressive symptoms are related to diminished DHEA levels, and studies have shown a positive relationship between DHEA and anterior pituitary volumes. However, specific links between responses to stress, DHEA levels, and anterior pituitary volume have not been established in developmental samples. METHODS: High-resolution T1-weighted MRI scans were collected from 137 healthy youth (9-17 years; Mage = 12.99 (SD = 1.87); 49% female; 85% White, 4% Indigenous, 1% Asian, 4% Black, 4% multiracial, 2% not reported). The anterior and posterior PGs were manually traced by trained raters. We examined the mediating effects of salivary DHEA on trauma-related symptoms (i.e., anxiety, depression, and posttraumatic) and PG volumes as well as an alternative model examining mediating effects of PG volume on DHEA and trauma-related symptoms. RESULTS: DHEA mediated the association between anxiety symptoms and anterior PG volume. Specifically, higher anxiety symptoms related to lower DHEA levels, which in turn were related to smaller anterior PG. CONCLUSIONS: These results shed light on the neurobiological sequelae of elevated anxiety in youth and are consistent with adult findings showing suppressed levels of DHEA in those with greater comorbid anxiety and depression. Specifically, adolescents with greater subclinical anxiety may exhibit diminished levels of DHEA during the pubertal window, which may be associated with disruptions in anterior PG growth.
Dehydroepiandrosterone , Hydrocortisone , Adult , Humans , Adolescent , Child , Female , Male , Hypothalamo-Hypophyseal System , Anxiety/diagnostic imaging , Pituitary-Adrenal System
Objective.Optically pumped magnetometers (OPMs) are emerging as a near-room-temperature alternative to superconducting quantum interference devices (SQUIDs) for magnetoencephalography (MEG). In contrast to SQUIDs, OPMs can be placed in a close proximity to subject's scalp potentially increasing the signal-to-noise ratio and spatial resolution of MEG. However, experimental demonstrations of these suggested benefits are still scarce. Here, to compare a 24-channel OPM-MEG system to a commercial whole-head SQUID system in a data-driven way, we quantified their performance in classifying single-trial evoked responses.Approach.We measured evoked responses to three auditory tones in six participants using both OPM- and SQUID-MEG systems. We performed pairwise temporal classification of the single-trial responses with linear discriminant analysis as well as multiclass classification with both EEGNet convolutional neural network and xDAWN decoding.Main results.OPMs provided higher classification accuracies than SQUIDs having a similar coverage of the left hemisphere of the participant. However, the SQUID sensors covering the whole helmet had classification scores larger than those of OPMs for two of the tone pairs, demonstrating the benefits of a whole-head measurement.Significance.The results demonstrate that the current OPM-MEG system provides high-quality data about the brain with room for improvement for high bandwidth non-invasive brain-computer interfacing.
The neural and cognitive processes underlying the flexible allocation of attention undergo a protracted developmental course with changes occurring throughout adolescence. Despite documented age-related improvements in attentional reorienting throughout childhood and adolescence, the neural correlates underlying such changes in reorienting remain unclear. Herein, we used magnetoencephalography (MEG) to examine neural dynamics during a Posner attention-reorienting task in 80 healthy youth (6-14 years old). The MEG data were examined in the time-frequency domain and significant oscillatory responses were imaged in anatomical space. During the reorienting of attention, youth recruited a distributed network of regions in the fronto-parietal network, along with higher-order visual regions within the theta (3-7 Hz) and alpha-beta (10-24 Hz) spectral windows. Beyond the expected developmental improvements in behavioral performance, we found stronger theta oscillatory activity as a function of age across a network of prefrontal brain regions irrespective of condition, as well as more limited age- and validity-related effects for alpha-beta responses. Distinct brain-behavior associations between theta oscillations and attention-related symptomology were also uncovered across a network of brain regions. Taken together, these data are the first to demonstrate developmental effects in the spectrally-specific neural oscillations serving the flexible allocation of attention.
Brain , Magnetoencephalography , Humans , Child , Adolescent , Brain/physiology , Magnetoencephalography/methods , Attention/physiology , Brain Mapping/methods
Introduction: Adolescence, a critical phase of human neurodevelopment, is marked by a tremendous reorganization of the brain and accompanied by improved cognitive performance. This development is driven in part by gene expression, which in turn is partly regulated by DNA methylation (DNAm). Methods: We collected brain imaging, cognitive assessments, and DNAm in a longitudinal cohort of approximately 200 typically developing participants, aged 9-14. This data, from three time points roughly 1 year apart, was used to explore the relationships between seven cytosine-phosphate-guanine (CpG) sites in genes highly expressed in brain tissues (GRIN2D, GABRB3, KCNC1, SLC12A9, CHD5, STXBP5, and NFASC), seven networks of grey matter (GM) volume change, and scores from seven cognitive tests. Results: The demethylation of the CpGs as well as the rates of change in DNAm were significantly related to improvements in total, crystalized, and fluid cognition scores, executive function, episodic memory, and processing speed, as well as several networks of GM volume increases and decreases that highlight typical patterns of brain maturation. Discussion: Our study provides a first look at the DNAm of genes involved in myelination, excitatory and inhibitory receptors, and connectivity, how they are related to the large-scale changes occurring in the brain structure as well as cognition during adolescence.
Over the past decade, transdiagnostic indicators in relation to neurobiological processes have provided extensive insight into youth's risk for psychopathology. During development, exposure to childhood trauma and dysregulation (i.e., so-called AAA symptomology: anxiety, aggression, and attention problems) puts individuals at a disproportionate risk for developing psychopathology and altered network-level neural functioning. Evidence for the latter has emerged from resting-state fMRI studies linking mental health symptoms and aberrations in functional networks (e.g., cognitive control (CCN), default mode networks (DMN)) in youth, although few of these investigations have used longitudinal designs. Herein, we leveraged a three-year longitudinal study to identify whether traumatic exposures and concomitant dysregulation trigger changes in the developmental trajectories of resting-state functional networks involved in cognitive control (N = 190; 91 females; time 1 Mage = 11.81). Findings from latent growth curve analyses revealed that greater trauma exposure predicted increasing connectivity between the CCN and DMN across time. Greater levels of dysregulation predicted reductions in within-network connectivity in the CCN. These findings presented in typically developing youth corroborate connectivity patterns reported in clinical populations, suggesting there is predictive utility in using transdiagnostic indicators to forecast alterations in resting-state networks implicated in psychopathology.
Introduction: The teratogenic effects of prenatal alcohol exposure (PAE) have been examined in animal models and humans. The current study extends the prior literature by quantifying differences in brain structure for individuals with a fetal alcohol spectrum disorder (FASD) compared to typically developing controls, as well as examining FASD subtypes. We hypothesized the FASD group would reveal smaller brain volume, reduced cortical thickness, and reduced surface area compared to controls, with the partial fetal alcohol syndrome (pFAS)/fetal alcohol syndrome (FAS) subtypes showing the largest effects and the PAE/alcohol-related neurodevelopmental disorder (ARND) subtype revealing intermediate effects. Methods: The sample consisted of 123 children and adolescents recruited from a single site including children with a diagnosis of FASD/PAE (26 males, 29 females) and controls (34 males, 34 females). Structural T1-weighted MRI scans were obtained on a 3T Trio TIM scanner and FreeSurfer v7.2 was used to quantify brain volume, cortical thickness, and surface area. Analyses examined effects by subgroup: pFAS/FAS (N = 32, Mage = 10.7 years, SEage = 0.79), PAE/ARND (N = 23, Mage = 10.8, SEage = 0.94), and controls (N = 68, Mage = 11.1, SEage = 0.54). Results: Total brain volume in children with an FASD was smaller relative to controls, but subtype analysis revealed only the pFAS/FAS group differed significantly from controls. Regional analyses similarly revealed reduced brain volume in frontal and temporal regions for children with pFAS/FAS, yet children diagnosed with PAE/ARND generally had similar volumes as controls. Notable differences to this pattern occurred in the cerebellum, caudate, and pallidum where children with pFAS/FAS and PAE/ARND revealed lower volume relative to controls. In the subset of participants who had neuropsychological testing, correlations between volume and IQ scores were observed. Goodness-of-Fit analysis by age revealed differences in developmental patterns (linear vs. quadratic) between groups in some cases. Discussion: This study confirmed prior results indicating decreased brain volume in children with an FASD and extended the results by demonstrating differential effects by structure for FASD subtypes. It provides further evidence for a complex role of PAE in structural brain development that is likely related to the cognitive and behavioral effects experienced by children with an FASD.
Assessing brain connectivity during rest has become a widely used approach to identify changes in functional brain organization during development. Generally, previous works have demonstrated that brain activity shifts from more local to more distributed processing from childhood into adolescence. However, the majority of those works have been based on functional magnetic resonance imaging measures, whereas multispectral functional connectivity, as measured using magnetoencephalography (MEG), has been far less characterized. In our study, we examined spontaneous cortical activity during eyes-closed rest using MEG in 101 typically developing youth (9-15 years old; 51 females, 50 males). Multispectral MEG images were computed, and connectivity was estimated in the canonical delta, theta, alpha, beta, and gamma bands using the imaginary part of the phase coherence, which was computed between 200 brain regions defined by the Schaefer cortical atlas. Delta and alpha connectivity matrices formed more communities as a function of increasing age. Connectivity weights predominantly decreased with age in both frequency bands; delta-band differences largely implicated limbic cortical regions and alpha band differences in attention and cognitive networks. These results are consistent with previous work, indicating the functional organization of the brain becomes more segregated across development, and highlight spectral specificity across different canonical networks.
Brain , Magnetoencephalography , Male , Female , Adolescent , Humans , Child , Brain/diagnostic imaging , Magnetoencephalography/methods , Brain Mapping/methods , Magnetic Resonance Imaging/methods , Limbic Lobe , Rest , Neural Pathways/diagnostic imaging
Recent investigations have studied the development of motor-related oscillatory responses to delineate maturational changes from childhood to young adulthood. While these studies included youth during the pubertal transition period, none have probed the impact of testosterone levels on motor cortical dynamics and performance. We collected salivary testosterone samples and recorded magnetoencephalography during a complex motor sequencing task in 58 youth aged 9-15 years old. The relationships between testosterone, age, task behavior, and beta (15-23 Hz) oscillatory dynamics were examined using multiple mediation modeling. We found that testosterone mediated the effect of age on movement-related beta activity. We also found that the effect of age on movement duration was mediated by testosterone and reaction time. Interestingly, the relationships between testosterone and motor performance were not mediated by beta activity in the left primary motor cortex, which may indicate the importance of higher-order motor regions. Overall, our results suggest that testosterone has unique associations with neural and behavioral indices of complex motor performance, beyond those already characterized in the literature. These findings are the first to link developmental changes in testosterone levels to maturation of beta oscillatory dynamics serving complex motor planning and execution, and specific measures of motor performance.
Magnetoencephalography , Motor Cortex , Humans , Adolescent , Young Adult , Adult , Child , Magnetoencephalography/methods , Movement/physiology , Reaction Time , Testosterone
Objectives.We aim to investigate the effects of head model inaccuracies on signal and source reconstruction accuracies for various sensor array distances to the head. This allows for the assessment of the importance of head modeling for next-generation magnetoencephalography (MEG) sensors, optically-pumped magnetometers (OPM).Approach.A 1-shell boundary element method (BEM) spherical head model with 642 vertices of radius 9 cm and conductivity of 0.33 S m-1was defined. The vertices were then randomly perturbed radially up to 2%, 4%, 6%, 8% and 10% of the radius. For each head perturbation case, the forward signal was calculated for dipolar sources located at 2 cm, 4 cm, 6 cm and 8 cm from the origin (center of the sphere), and for a 324 sensor array located at 10 cm to 15 cm from the origin. Equivalent current dipole (ECD) source localization was performed for each of these forward signals. The signal for each perturbed spherical head case was then analyzed in the spatial frequency domain, and the signal and ECD errors were quantified relative to the unperturbed case.Main results.In the noiseless and high signal-to-noise ratio (SNR) case of approximately ≥6 dB, inaccuracies in our spherical BEM head conductor models lead to increased signal and ECD inaccuracies when sensor arrays are placed closer to the head. This is true especially in the case of deep and superficial sources. In the noisy case however, the higher SNR for closer sensor arrays allows for an improved ECD fit and outweighs the effects of head geometry inaccuracies.Significance.OPMs may be placed directly on the head, as opposed to the more commonly used superconducting quantum interference device sensors which must be placed a few centimeters away from the head. OPMs thus allow for signals of higher spatial resolution to be captured, resulting in potentially more accurate source localizations. Our results suggest that an increased emphasis on accurate head modeling for OPMs may be necessary to fully realize its improved source localization potential.
Head , Magnetoencephalography , Electric Conductivity , Signal-To-Noise Ratio , Brain
OBJECTIVE: Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets. METHODS: LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. RESULTS: LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. CONCLUSION: We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. SIGNIFICANCE: We propose a novel prediction algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work can lead to new insights in both algorithm design and neuroscience research.
Algorithms , Brain , Reproducibility of Results , Brain/diagnostic imaging , Magnetic Resonance Imaging/methods , Phenotype
OBJECTIVE: Multimodal-based methods show great potential for neuroscience studies by integrating complementary information. There has been less multimodal work focussed on brain developmental changes. METHODS: We propose an explainable multimodal deep dictionary learning method to uncover both the commonality and specificity of different modalities, which learns the shared dictionary and the modality-specific sparse representations based on the multimodal data and their encodings of a sparse deep autoencoder. RESULTS: By regarding three fMRI paradigms collected during two tasks and resting state as modalities, we apply the proposed method on multimodal data to identify the brain developmental differences. The results show that the proposed model can not only achieve better performance in reconstruction, but also yield age-related differences in reoccurring patterns. Specifically, both children and young adults prefer to switch among states during two tasks while staying within a particular state during rest, but the difference is that children possess more diffuse functional connectivity patterns while young adults have more focused functional connectivity patterns. CONCLUSION AND SIGNIFICANCE: To uncover the commonality and specificity of three fMRI paradigms to developmental differences, multimodal data and their encodings are used to train the shared dictionary and the modality-specific sparse representations. Identifying brain network differences helps to understand how the neural circuits and brain networks form and develop with age.
Brain , Magnetic Resonance Imaging , Young Adult , Child , Humans , Magnetic Resonance Imaging/methods , Brain/diagnostic imaging , Brain Mapping/methods , Learning , Rest
The default mode network (DMN) plays a crucial role in internal self-processing, rumination, and social functions. Disruptions to DMN connectivity have been linked with early adversity and the emergence of psychopathology in adolescence and early adulthood. Herein, we investigate how subclinical psychiatric symptoms can impact DMN functional connectivity during the pubertal transition. Resting-state fMRI data were collected annually from 190 typically-developing youth (9-15 years-old) at three timepoints and within-network DMN connectivity was computed. We used latent growth curve modeling to determine how self-reported depressive and posttraumatic stress symptoms predicted rates of change in DMN connectivity over the three-year period. In the baseline model without predictors, we found no systematic changes in DMN connectivity over time. However, significant modulation emerged after adding psychopathology predictors; greater depressive symptomatology was associated with significant decreases in connectivity over time, whereas posttraumatic stress symptoms were associated with significant increases in connectivity over time. Follow-up analyses revealed that these effects were driven by connectivity changes involving the dorsal medial prefrontal cortex subnetwork. In conclusion, these data suggest that subclinical depressive and posttraumatic symptoms alter the trajectory of DMN connectivity, which may indicate that this network is a nexus of clinical significance in mental health disorders.
Problem Behavior , Stress Disorders, Post-Traumatic , Humans , Adolescent , Adult , Child , Stress Disorders, Post-Traumatic/pathology , Default Mode Network , Prefrontal Cortex , Magnetic Resonance Imaging , Brain , Brain Mapping
