Resting-state Functional Connectivity Patterns Research Articles

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea.

Primary dysmenorrhea (PDM) is a common complaint in women throughout the menstrual years. Acupuncture has been shown to be effective in dysmenorrhea; however, there are large interindividual differences in patients’ responses to acupuncture treatment. Fifty-four patients with PDM were recruited and randomized into real or sham acupuncture treatment groups (over the course of three menstrual cycles). Pain-related functional connectivity (FC) matrices were constructed at baseline and post-treatment period. The different neural mechanisms altered by real and sham acupuncture were detected with multivariate analysis of variance. Multivariate pattern analysis (MVPA) based on a machine learning approach was used to explore whether the different FC patterns predicted the acupuncture treatment response in the PDM patients. The results showed that real but not sham acupuncture significantly relieved pain severity in PDM patients. Real and sham acupuncture displayed differences in FC alterations between the descending pain modulatory system (DPMS) and sensorimotor network (SMN), the salience network (SN) and SMN, and the SN and default mode network (DMN). Furthermore, MVPA found that these FC patterns at baseline could predict the acupuncture treatment response in PDM patients. The present study verified differentially altered brain mechanisms underlying real and sham acupuncture in PDM patients and supported the use of neuroimaging biomarkers for individual-based precise acupuncture treatment in patients with PDM.

Spontaneous cognition and its relationship to human creativity: A functional connectivity study involving a chain free association task

Resting-state functional connectivity (RSFC) between various brain regions is thought to be associated with creative abilities. Extensive research correlating RSFC with performance on creativity tasks has revealed some of the RSFC patterns characterizing ‘the creative brain’. Yet, our understanding of the neurocognitive processes underlying creative thinking still remains limited. This limitation results, in part, from the fact that standard creativity tasks used in these studies do not distinguish between the different modes of cognitive processing that are critical in creative cognition (e.g., spontaneous cognition vs. controlled cognition). In the present fMRI research we address this limitation by using a chain free association task ​− ​a task that we have recently refined and validated for the purpose of isolating measures of spontaneous cognition that are relevant for creative thinking (referred to as associative fluency and associative flexibility). In our study, 27 female participants completed standardized creativity tasks, a chain free association task, and a fMRI scan in which RSFC was measured. Our results indicate that higher scores on associative fluency are associated with stronger positive RSFC within the default mode network (DMN; i.e., between DMN regions). Critically, we provide evidence that the previously-identified relationship between performance on creativity tasks and connectivity within the DMN is partially mediated by associative fluency. Thus, our observations suggest that the heightened DMN connectivity observed in ‘the creative brain’ can be explained, at least to some extent, by spontaneous cognition. Overall, our study identifies unique RSFC patterns that are related specifically to spontaneous cognitive processes involved in creative ideation, thus shedding new light on mechanisms of creative processing.

Gradients of functional organization in posterior parietal cortex revealed by visual attention, visual short-term memory, and intrinsic functional connectivity

Visual attention and visual working memory tasks recruit a common network of lateral frontal cortical (LFC) and posterior parietal cortical (PPC) regions. Here, we examine finer-scale organization of this frontoparietal network. Three LFC regions recruited by visual cognition tasks, superior precentral sulcus (sPCS), inferior precentral sulcus (iPCS), and mid inferior frontal sulcus (midIFS) exhibit differential patterns of resting-state functional connectivity to PPC. A broad dorsomedial to ventrolateral gradient is observed, with sPCS connectivity dominating in the dorsomedial PPC band, iPCS dominating in the middle band, and midIFS dominating in the ventrolateral band. These connectivity-defined subregions of PPC capture differential task activation between a pair of visual attention and working memory tasks. The relative functional connectivity of sPCS and iPCS also varies along the rostral-caudal axis of the retinotopic regions of PPC. iPCS connectivity is relatively stronger near the IPS0/IPS1 and IPS2/IPS3 borders, especially on the lateral portions of these borders, which each preferentially encode central visual field representations. In contrast, sPCS connectivity is relatively stronger elsewhere in retinotopic IPS regions which preferentially encode peripheral visual field representations. These findings reveal fine-scale gradients in functional connectivity within the frontoparietal visual network that capture a high-degree of specificity in PPC functional organization.

Resting-State SEEG May Help Localize Epileptogenic Brain Regions.

Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P<.05) and between epileptogenic areas and other structures (P<.01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P<.01) and betweenness centrality (P<.01), and greater decay of functional connectivity with distance (P<.05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

Neural connectivity biotypes: associations with internalizing problems throughout adolescence.

Neurophysiological patterns may distinguish which youth are at risk for the well-documented increase in internalizing symptoms during adolescence. Adolescents with internalizing problems exhibit altered resting-state functional connectivity (RSFC) of brain regions involved in socio-affective processing. Whether connectivity-based biotypes differentiate adolescents' levels of internalizing problems remains unknown. Sixty-eight adolescents (37 females) reported on their internalizing problems at ages 14, 16, and 18 years. A resting-state functional neuroimaging scan was collected at age 16. Time-series data of 15 internalizing-relevant brain regions were entered into the Subgroup-Group Iterative Multi-Model Estimation program to identify subgroups based on RSFC maps. Associations between internalizing problems and connectivity-based biotypes were tested with regression analyses. Two connectivity-based biotypes were found: a Diffusely-connected biotype (N = 46), with long-range fronto-parietal paths, and a Hyper-connected biotype (N = 22), with paths between subcortical and medial frontal areas (e.g. affective and default-mode network regions). Higher levels of past (age 14) internalizing problems predicted a greater likelihood of belonging to the Hyper-connected biotype at age 16. The Hyper-connected biotype showed higher levels of concurrent problems (age 16) and future (age 18) internalizing problems. Differential patterns of RSFC among socio-affective brain regions were predicted by earlier internalizing problems and predicted future internalizing problems in adolescence. Measuring connectivity-based biotypes in adolescence may offer insight into which youth face an elevated risk for internalizing disorders during this critical developmental period.

Hippocampal Resting-State Functional Connectivity Patterns are More Closely Associated with Severity of Subjective Memory Decline than Whole Hippocampal and Subfield Volumes.

The goal of this study was to examine whether hippocampal volume or resting-state functional connectivity (rsFC) patterns are associated with subjective memory decline (SMD) in cognitively normal aged adults. Magnetic resonance imaging data from 53 participants (mean age: 71.9 years) of the Boston University Alzheimer’s Disease Center registry were used in this cross-sectional study. Separate analyses treating SMD as a binary and continuous variable were performed. Subfield volumes were generated using FreeSurfer v6.0, and rsFC strength between the head and body of the hippocampus and the rest of the brain was calculated. Decreased left whole hippocampal volume and weaker rsFC strength between the right body of the hippocampus and the default mode network (DMN) were found in SMD+. Cognitive Change Index score was not correlated with volumetric measures but was inversely correlated with rsFC strength between the right body of the hippocampus and 6 brain networks, including the DMN, task control, and attentional networks. These findings suggest that hippocampal rsFC patterns reflect the current state of SMD in cognitively normal adults and may reflect subtle memory changes that standard neuropsychological tests are unable to capture.

Research Review: Brain network connectivity and the heterogeneity of depression in adolescence - a precision mental health perspective.

Adolescence is a period of high risk for the onset of depression, characterized by variability in symptoms, severity, and course. During adolescence, the neurocircuitry implicated in depression continues to mature, suggesting that it is an important period for intervention. Reflecting the recent emergence of 'precision mental health' - a person-centered approach to identifying, preventing, and treating psychopathology - researchers have begun to document associations between heterogeneity in features of depression and individual differences in brain circuitry, most frequently in resting-state functional connectivity (RSFC). In this review, we present emerging work examining pre- and post-treatment measures of network connectivity in depressed adolescents; these studies reveal potential intervention-specific neural markers of treatment efficacy. We also review findings from studies examining associations between network connectivity and both types of depressive symptoms and response to treatment in adults, and indicate how this work can be extended to depressed adolescents. Finally, we offer recommendations for research that we believe will advance the science of precision mental health of adolescence. Nascent studies suggest that linking RSFC-based pathophysiological variation with effects of different types of treatment and changes in mood following specific interventions will strengthen predictions of prognosis and treatment response. Studies with larger sample sizes and direct comparisons of treatments are required to determine whether RSFC patterns are reliable neuromarkers of treatment response for depressed adolescents. Although we are not yet at the point of using RSFC to guide clinical decision-making, findings from research examining the stability and reliability of RSFC point to a favorable future for network-based clinical phenotyping. Delineating the correspondence between specific clinical characteristics of depression (e.g., symptoms, severity, and treatment response) and patterns of network-based connectivity will facilitate the development of more tailored and effective approaches to the assessment, prevention, and treatment of depression in adolescents.

Altered Temporal Variability of Local and Large-Scale Resting-State Brain Functional Connectivity Patterns in Schizophrenia and Bipolar Disorder.

Schizophrenia and bipolar disorder share some common clinical features and are both characterized by aberrant resting-state functional connectivity (FC). However, little is known about the common and specific aberrant features of the dynamic FC patterns in these two disorders. In this study, we explored the differences in dynamic FC among schizophrenia patients (n = 66), type I bipolar disorder patients (n = 53), and healthy controls (n = 66), by comparing temporal variabilities of FC patterns involved in specific brain regions and large-scale brain networks. Compared with healthy controls, both patient groups showed significantly increased regional FC variabilities in subcortical areas including the thalamus and basal ganglia, as well as increased inter-network FC variability between the thalamus and sensorimotor areas. Specifically, more widespread changes were found in the schizophrenia group, involving increased FC variabilities in sensorimotor, visual, attention, limbic and subcortical areas at both regional and network levels, as well as decreased regional FC variabilities in the default-mode areas. The observed alterations shared by schizophrenia and bipolar disorder may help to explain their overlapped clinical features; meanwhile, the schizophrenia-specific abnormalities in a wider range may support that schizophrenia is associated with more severe functional brain deficits than bipolar disorder. Together, these findings highlight the potentials of using dynamic FC as an objective biomarker for the monitoring and diagnosis of either schizophrenia or bipolar disorder.

Connectome-based models can predict early symptom improvement in major depressive disorder

BackgroundMajor depressive disorder (MDD) is a debilitating mental illness with more than 50% of patients not achieving an adequate response using first-line treatments. Reliable models that predict antidepressant treatment outcome are needed to guide clinical decision making. We aimed to build predictive models of treatment improvement for MDD patients using machine learning approaches based on fMRI resting-state functional connectivity patterns. MethodsResting-state fMRI data were acquired from 192 untreated MDD patients at recruitment, and their severity of depression was assessed by Hamilton Rating Scale for Depression (HAMD) at baseline. Patients were given medication after the initial MR scan and their symptoms were monitored through HAMD for a period of six months. Connectome-based predictive modeling (CPM) algorithms were implemented to predict the improvement in HAMD score at one month from resting-state connectivity at baseline. Additionally, by selectively combining the features from all leave-one-out iterations in the model building stage, we created a consensus model that could be generalized to predict improvement in HAMD score in samples of non-overlapping subjects at different time points. ResultsUsing baseline functional connectivity, CPM successfully predicted symptom improvement of depression at one month. In addition, a consensus ‘MDD improvement model’ could predict symptom improvement for novel individuals at the two-week, one-month, two-month and three-month time points after antidepressant treatment. ConclusionsIndividual pre-treatment functional brain networks contain meaningful information that can be gleaned to build predictors of treatment outcome. The identified MDD improvement networks could be an appropriate biomarker for predicting individual therapeutic response of antidepressant treatment. Replication and validation using other large datasets will be a key next step before these models can be used in clinical practice.

Disruption within brain default mode network in postpartum women without depression.

Previous studies have demonstrated that cognitive dysfunction is associated with neurophysiological changes in postpartum period. This study aimed to investigate the intrinsic functional connectivity (FC) pattern within the default mode network (DMN) and its associations with cognitive dysfunction in postpartum women without depression revealed by resting-state functional magnetic resonance imaging (fMRI).Resting-state fMRI scans were acquired from 21 postpartum women and 21 age- and education-matched nulliparous women. The posterior cingulate cortex (PCC) was selected as the seed region to detect the FC patterns and then determine whether these changes were related to specific cognitive performance.Compared with the nulliparous women, postpartum women had a significantly decreased FC between the PCC and the left medial prefrontal cortex (mPFC). After correcting for age and education, the reduced FC between the PCC and the left mPFC was positively correlated with the poorer Clock-Drawing Test (CDT) scores in postpartum women (r = 0.742, P < .001).The present study mainly demonstrated decreased resting-state FC pattern within the DMN regions that was linked with impaired cognitive function in postpartum women. These findings illustrated the potential role of the DMN in postpartum women that will provide novel insight into the underlying neuropathological mechanisms in postpartum period.

Diagnostic and Predictive Neuroimaging Biomarkers for Posttraumatic Stress Disorder

BackgroundComorbidity between posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) has been commonly overlooked by studies examining resting-state functional connectivity patterns in PTSD. The current study used a data-driven approach to identify resting-state functional connectivity biomarkers to 1) differentiate individuals with PTSD (with or without MDD) from trauma-exposed healthy control subjects (TEHCs), 2) compare individuals with PTSD alone with those with comorbid PTSD+MDD, and 3) explore the clinical utility of the identified biomarkers by testing their associations with clinical symptoms and treatment response. MethodsResting-state magnetic resonance images were obtained from 51 individuals with PTSD alone, 52 individuals with PTSD+MDD, and 76 TEHCs. Of the 103 individuals with PTSD, 55 were enrolled in prolonged exposure treatment. A support vector machine model was used to identify resting-state functional connectivity biomarkers differentiating individuals with PTSD (with or without MDD) from TEHCs and differentiating individuals with PTSD alone from those with PTSD+MDD. The associations between the identified features and symptomatology were tested with Pearson correlations. ResultsThe support vector machine model achieved 70.6% accuracy in discriminating between individuals with PTSD and TEHCs and achieved 76.7% accuracy in discriminating between individuals with PTSD alone and those with PTSD+MDD for out-of-sample prediction. Within-network connectivity in the executive control network, prefrontal network, and salience network discriminated individuals with PTSD from TEHCs. The basal ganglia network played an important role in differentiating individuals with PTSD alone from those with PTSD+MDD. PTSD scores were inversely correlated with within–executive control network connectivity (p < .001), and executive control network connectivity was positively correlated with treatment response (p < .001). ConclusionsResults suggest that unique brain-based abnormalities differentiate individuals with PTSD from TEHCs, differentiate individuals with PTSD from those with PTSD+MDD, and demonstrate clinical utility in predicting levels of symptomatology and treatment response.

P177 Resting-state functional connectivity pattern predicts the outcome in cognitive training combined with transcranial electrical stimulation

P177 Resting-state functional connectivity pattern predicts the outcome in cognitive training combined with transcranial electrical stimulation

Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach.

PurposeIn recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from whole-brain resting-state functional connectivity patterns using machine learning.Patients and MethodsWe measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests.ResultsThe results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain–behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD (P < 0.05).ConclusionThis study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD.

Underconnectivity Between Visual and Salience Networks and Links With Sensory Abnormalities in Autism Spectrum Disorders

The anterior insular cortex (AI), which is a part of the salience network, is critically involved in visual awareness, multisensory perception, and social and emotional processing, among other functions. In children and adolescents with autism spectrum disorders (ASDs), evidence has suggested aberrant functional connectivity (FC) of AI compared with typically developing peers. While recent studies have primarily focused on the functional connections between salience and social networks, much less is known about connectivity between AI and primary sensory regions, including visual areas, and how these patterns may be linked to autism symptomatology. The current investigation implemented functional magnetic resonance imaging to examine resting-state FC patterns of salience and visual networks in children and adolescents with ASDs compared with typically developing controls, and to relate them to behavioral measures. Functional underconnectivity was found in the ASD group between left AI and bilateral visual cortices. Moreover, in an ASD subgroup with more atypical visual sensory profiles, FC was positively correlated with abnormal social motivational responsivity. Findings of reduced FC between salience and visual networks in ASDs potentially indicate deficient selection of salient information. Moreover, in children and adolescents with ASDs who show strongly atypical visual sensory profiles, connectivity at seemingly more neurotypical levels may be paradoxically associated with greater impairment of social motivation.

Influence of Gonadotropin Hormone Releasing Hormone Agonists on Interhemispheric Functional Connectivity in Girls With Idiopathic Central Precocious Puberty.

Purpose: The pubertal growth suppressive effects of gonadotropin hormone releasing hormone agonists (GnRHa) are well-known, although it remains unclear if long-term GnRHa treatment influences the brain function of treated children. The present study investigated the differences in the homotopic resting-state functional connectivity patterns in girls with idiopathic central precocious puberty (ICPP) with and without GnRHa treatment using voxel-mirrored homotopic connectivity (VMHC).Methods: Eighteen girls with ICPP who underwent 12 months of GnRHa treatment, 40 treatment-naïve girls with ICPP, and 19 age-matched girls with premature thelarche underwent resting-state functional magnetic resonance imaging using a 3T MRI. VMHC method was performed to explore the differences in the resting-state interhemispheric functional connectivity. The levels of serum pubertal hormones, including luteinizing hormone (LH), follicular-stimulating hormone, and estradiol, were assessed. Correlation analyses among the results of clinical laboratory examinations, neuropsychological scales, and VMHC values of different brain regions were performed with the data of the GnRHa treated group.Results: Significant decreases in VMHC of the lingual, calcarine, superior temporal, and middle frontal gyri were identified in the untreated group, compared with the control group. Medicated patients showed decreased VMHC in the superior temporal gyrus, when compared with the controls. Compared to the unmedicated group, the medicated group showed a significant increase in VMHC in the calcarine and middle occipital gyrus. Moreover, a positive correlation was observed between basal LH levels and VMHC of the middle occipital gyrus in medicated patients.Conclusions: These findings indicate that long-term treatment with GnRHa was associated with increased interhemispheric functional connectivity within several areas responsible for memory and visual process in patients with ICPP. Higher interhemispheric functional connectivity in the middle occipital gyrus was related to higher basal LH production in the girls who underwent treatment. The present study adds to the growing body of research associated with the effects of GnRHa on brain function.

Density center-based fast clustering of widefield fluorescence imaging of cortical mesoscale functional connectivity and relation to structural connectivity.

.Spontaneous resting-state neural activity or hemodynamics has been used to reveal functional connectivity in the brain. However, most of the commonly used clustering algorithms for functional parcellation are time-consuming, especially for high-resolution imaging data. We propose a density center-based fast clustering (DCBFC) method that can rapidly perform the functional parcellation of isocortex. DCBFC was validated using both simulation data and the spontaneous calcium signals from widefield fluorescence imaging of excitatory neuron-expressing transgenic mice (Vglut2-GCaMP6s). Compared to commonly used clustering methods such as k-means, hierarchical, and spectral, DCBFC showed a higher adjusted Rand index when the signal-to-noise ratio was greater than for simulated data and higher silhouette coefficient for in vivo mouse data. The resting-state functional connectivity (RSFC) patterns obtained by DCBFC were compared with the anatomic axonal projection density (PDs) maps derived from the voxel-scale model. The results showed a high spatial correlation between RSFC patterns and PDs.

Altered functional connectivity of the nucleus accumbens subdivisions in amphetamine-type stimulant abusers: a resting-state fMRI study

BackgroundThe growing abuse of amphetamine-type stimulants leads to new challenges to human health. A possible addiction mechanism has been proposed by altered functional architecture of the nucleus accumbens (NAc) during resting state. NAc contains different subdivisions and they may play different roles in addiction. The aim of the present study was to examine whether there are common or distinct patterns of functional connectivity of the NAc subdivisions in amphetamine-type stimulant abusers (ATSAs).MethodsThe present study recruited 17 male ATSAs and 22 healthy male controls. All the subjects underwent resting-state functional magnetic resonance imaging (fMRI) with their eyes closed. The NAc was divided into core-like and shell-like subdivisions. We used seed-based resting-state functional connectivity (RSFC) analyses to identify differences in brain functional architecture between ATSAs and healthy controls (HCs).ResultsATSAs had lower positive RSFCs with all of the NAc subdivisions over the left orbital part of superior frontal gyrus and higher positive RSFCs with the NAc subdivisions over the left opercular part of inferior frontal gyrus than HCs, which indicates common abnormalities across the NAc subdivisions in ATSAs. In addition, the RSFCs between the NAc subdivisions and the left orbital part of superior frontal gyrus were negatively correlated with the addiction severity in ATSAs.ConclusionThese results provide evidence that there are common RSFC patterns of the NAc subdivisions in ATSAs. The abnormality indicated by disrupted functional connectivity between the NAc subdivisions and prefrontal cortex suggests abnormal interaction between the rewarding process and cognitive control in ATSAs. Our results shed insight on the neurobiological mechanisms of ATSA and suggest potential novel therapeutic targets for treatment and intervention of ATSAs.

Weaker Inter-hemispheric and Local Functional Connectivity of the Somatomotor Cortex During a Motor Skill Acquisition Is Associated With Better Learning.

Recently, an increasing interest in investigating interactions between brain regions using functional connectivity (FC) methods has shifted the initial focus of cognitive neuroimaging research from localizing functional circuits based on task activation to mapping brain networks based on intrinsic FC dynamics. Leveraging the advantages of the latter approach, it has been shown that despite primarily invariant intrinsic organization of the large-scale functional networks, interactions between and within these networks significantly differ between various behavioral and cognitive states. These differences presumably indicate transient reconfiguration of functional connections—an instantaneous process that flexibly mediates and calibrates human behavior according to momentary demands of the environment. Nevertheless, the specificity of these reconfigured FC patterns to the task at hand and their relevance to adaptive processes during learning remain elusive. To address this knowledge gap, we investigated (1) to what extent FC within the somatomotor network is reconfigured during motor skill practice, and (2) how these changes are related to learning. We applied a seed-driven FC approach to data collected during a continuous task-free condition, so-called resting state, and during a motor sequence learning task using functional magnetic resonance imaging. During the task, participants repeatedly performed a short five-element sequence with their non-dominant (left) hand. As predicted, such unimanual sequence production was associated with lateralized activation of the right somatomotor cortex (SMC). Using this “active” region as a seed, here we show that unimanual performance of the motor sequence relies on functional segregation between the two SMC and selective integration between the “active” SMC and supplementary motor area. Whereas, greater segregation between the two SMC was associated with gains in performance rate, greater segregation within the “active” SMC itself was associated with more consistent performance by the end of training. Nether the resting-state FC patterns within the somatomotor network nor their relative modulation by the task state predicted these behavioral benefits of learning. Our results suggest that task-induced FC changes reflect reconfiguration of the connectivity patterns within the somatomotor network rather than a simple amplification or silencing of its intrinsic dynamics. Such reconfiguration not only supports motor behavior but may also predict learning.

Reconciling Dimensional and Categorical Models of Autism Heterogeneity: A Brain Connectomics and Behavioral Study

BackgroundHeterogeneity in autism spectrum disorder (ASD) has hindered the development of biomarkers, thus motivating subtyping efforts. Most subtyping studies divide individuals with ASD into nonoverlapping (categorical) subgroups. However, continuous interindividual variation in ASD suggests that there is a need for a dimensional approach. MethodsA Bayesian model was employed to decompose resting-state functional connectivity (RSFC) of individuals with ASD into multiple abnormal RSFC patterns, i.e., categorical subtypes, henceforth referred to as “factors.” Importantly, the model allowed each individual to express one or more factors to varying degrees (dimensional subtyping). The model was applied to 306 individuals with ASD (5.2–57 years of age) from two multisite repositories. Post hoc analyses associated factors with symptoms and demographics. ResultsAnalyses yielded three factors with dissociable whole-brain hypo- and hyper–RSFC patterns. Most participants expressed multiple (categorical) factors, suggestive of a mosaic of subtypes within individuals. All factors shared abnormal RSFC involving the default mode network, but the directionality (hypo- or hyper–RSFC) differed across factors. Factor 1 was associated with core ASD symptoms. Factors 1 and 2 were associated with distinct comorbid symptoms. Older male participants preferentially expressed factor 3. Factors were robust across control analyses and were not associated with IQ or head motion. ConclusionsThere exist at least three ASD factors with dissociable whole-brain RSFC patterns, behaviors, and demographics. Heterogeneous default mode network hypo- and hyper–RSFC across the factors might explain previously reported inconsistencies. The factors differentiated between core ASD and comorbid symptoms—a less appreciated domain of heterogeneity in ASD. These factors are coexpressed in individuals with ASD with different degrees, thus reconciling categorical and dimensional perspectives of ASD heterogeneity.

Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics

There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.